From d979b6b95b579d2cf340f47a7010efc4793f0c14 Mon Sep 17 00:00:00 2001
From: Kevin Johnson <appliedmathkj@gmail.com>
Date: Wed, 8 Dec 2021 14:26:27 -0800
Subject: [PATCH 1/6] DEV jnb dev

---
 jnb/pta_streamlit.py     | 1076 ++++++++++++++++++++++++++++++++++++++
 jnb/sample.json          |   20 +
 jnb/simple_strategy.json |   12 +
 jnb/talit.py             |  499 ++++++++++++++++++
 jnb/test.json            |   10 +
 5 files changed, 1617 insertions(+)
 create mode 100644 jnb/pta_streamlit.py
 create mode 100644 jnb/sample.json
 create mode 100644 jnb/simple_strategy.json
 create mode 100644 jnb/talit.py
 create mode 100644 jnb/test.json

diff --git a/jnb/pta_streamlit.py b/jnb/pta_streamlit.py
new file mode 100644
index 0000000..7234f37
--- /dev/null
+++ b/jnb/pta_streamlit.py
@@ -0,0 +1,1076 @@
+# -*- coding: utf-8 -*-
+import datetime
+from os import read
+from pathlib import Path
+from json import load as json_load
+from json import loads as json_loads
+from json import dump as json_dump
+from json import dumps as json_dumps
+
+from time import sleep as tsleep
+from matplotlib.pyplot import axis
+
+import numpy as np
+import pandas as pd
+
+# import matplotlib.pyplot as plt
+# import matplotlib.colors as colors
+# from matplotlib import rc
+# import mplfinance as mpf
+# from tqdm.notebook import trange, tqdm
+import streamlit as st
+from streamlit.state.session_state import SessionState
+
+import pandas_ta as ta
+
+
+dt_today = datetime.datetime.now()
+e = pd.DataFrame()
+
+pta_session = {}
+
+strategy_sample = \
+"""{
+    "name": "Sample JSON Strategy",
+    "description": "Sample JSON Strategy Test",
+    "ta": [
+        {"kind": "ema", "length": 10, "sma": false},
+        {"kind": "sma", "length": 50, "talib": false},
+        {
+            "kind": "bbands",
+            "talib": true,
+            "col_numbers": [0, 1, 2],
+            "col_names": ["BBL", "BBM", "BBU"]
+        },
+        {
+            "kind": "atr",
+            "length": 50,
+            "talib": true,
+            "col_names": ["ATR"]
+        }
+    ]
+}"""
+
+if "pta" not in st.session_state:
+    st.session_state["pta"] = pta_session
+
+print(f"[!] st.session_state: {st.session_state}\n")
+
+
+
+def _chart():
+    pass
+
+def _count_down_container(message:str = None, n:int = 1):
+    """https://docs.streamlit.io/library/api-reference/layout/st.empty"""
+    with st.empty():
+        for seconds in range(n):
+            if message is None:
+                st.write(f"⏳ {seconds} seconds have passed")
+            else:
+                st.write(message)
+            tsleep(1)
+
+        if message is None: st.write("✔️ Times up!")
+        st.empty()
+
+
+def _is_st_column(column):
+    if column is None: return st
+    return column if isinstance(column, st._DeltaGenerator) else st
+
+
+@st.cache(ttl=60, show_spinner=True)
+def _load_file(file):
+    """Load file of type st.UploadedFile"""
+    suffix = file.name.split(".")[1]
+    result = None
+    if suffix == "csv":
+        result = pd.read_csv(file)
+    elif suffix == "json":
+        result = pd.read_json(file)
+    elif suffix == "pkl":
+        result = pd.read_pickle(file)
+    else: # Default
+        result = pd.read_csv(file)
+    return result
+
+
+def _max_width_(pct_width:int = None):
+    """"https://discuss.streamlit.io/t/custom-render-widths/81/8"""
+    if pct_width is None:
+        max_width_str = f"max-width: 2000px;"
+    else:
+        max_width_str = f"max-width: {pct_width}%;"
+
+    st.markdown(
+        f"""
+    <style>
+    .reportview-container .main .block-container{{
+        {max_width_str}
+    }}
+    </
+    """, unsafe_allow_html=True)
+
+
+def _success(msg:str = "Success!", secs:float = 1.25):
+    ready = st.success(msg)
+    tsleep(secs)
+    ready.empty()
+
+
+def _talist2tuple(ta_list:list):
+    """Convert "col_numbers" and "col_names" list values to tuples"""
+    for indicator in ta_list:
+        if "col_numbers" in indicator and len(indicator["col_numbers"]):
+            indicator["col_numbers"] = tuple(indicator["col_numbers"])
+        if "col_names" in indicator and len(indicator["col_names"]):
+            indicator["col_names"] = tuple(indicator["col_names"])
+    return ta_list
+
+
+def permute_col(x: pd.Series):
+    """Permute values in a Pandas Series"""
+    return np.random.permutation(x)
+
+def shuffle_col(x: pd.Series, frac:float = 1.0):
+    """Shuffle values and reset index in a Pandas Series"""
+    _ = x.sample(frac=frac)
+    _.index = x.index
+    return _
+
+
+def check_strategy(json_text, as_bool:bool = False):
+    strat = json_loads(json_text)
+    if "name" not in strat or "ta" not in strat: return False
+
+    if isinstance(strat["ta"], list) and len(strat["ta"]) > 0:
+        strat["ta"] = _talist2tuple(strat["ta"])
+    else:
+        return False
+
+    result = None
+    if "description" in strat:
+        result = ta.Strategy(name=strat["name"], ta=strat["ta"], description=strat["description"])
+    else:
+        result = ta.Strategy(name=strat["name"], ta=strat["ta"])
+
+    if as_bool:
+        return True if result is not None else False
+    return result
+
+
+def exclude_indicators():
+    return [
+        "above", "above_value", "below", "below_value", "cross", "cross_value"
+    ]
+
+
+def load_strategy(path:str):
+    strat, json_strat = None, json_load(open(path))
+    strat = json_strat[0] if isinstance(json_strat, list) and len(json_strat) == 1 else json_strat
+
+    for indicator in strat["ta"]:
+        if "col_numbers" in indicator and len(indicator["col_numbers"]):
+            indicator["col_numbers"] = tuple(indicator["col_numbers"])
+        if "col_names" in indicator and len(indicator["col_names"]):
+            indicator["col_names"] = tuple(indicator["col_names"])
+
+    if strat is not None and hasattr(strat, "description") and len(strat):
+        return ta.Strategy(name=strat["name"], ta=strat["ta"], description=strat["description"])
+    else:
+        return ta.Strategy(name=strat["name"], ta=strat["ta"])
+
+
+def localfile_data(title="### Upload"):
+    # st.markdown(title)
+    base = st.container()
+    with base:
+        parameters = st.container()
+        with parameters:
+
+            # st.markdown("#### Parameters")
+
+            # lf_p11, lf_p12, lf_p13 = st.columns(3)
+
+            # fpath = lf_p11.text_input("Directory", ".", key="local_dir")
+            # file_suffix = lf_p12.selectbox(f"File Type", ["csv", "json"])
+            # fpath = return_file(fpath, file_suffix)
+            # file_ = lf_p13.selectbox(f"{file_suffix.upper()} File", fpath)
+
+
+            # uploaded_file = st.file_uploader("Choose a file", type=["csv", "json"], key="uploadfile")
+            # lf_p21, lf_p22 = st.columns(2)
+            localdf = pd.DataFrame()
+            file_ = st.file_uploader("Choose a file", type=["csv", "json"], key="uploaded_file")
+            if file_ is not None:
+                file_suffix = file_.name.split(".")[1]
+                # fd = file_.read()
+                # st.write([x for x in dir(file_) if not x.startswith('_')])
+                _success(f"{file_.name} Ready")
+
+                if "local_file_name" not in st.session_state["pta"]:
+                    st.session_state["pta"]["local_file_name"] = file_.name
+
+                # st.write(st.session_state)
+
+                if file_suffix == "csv":
+                    localdf = pd.read_csv(file_)
+                elif file_suffix == "json":
+                    localdf = pd.read_json(file_)
+                else: pass
+
+                if "localdf" not in st.session_state["pta"]:
+                    st.session_state["pta"]["localdf"] = localdf
+
+            if not localdf.empty:
+                st.markdown("### Chart")
+                localdf.name = Path(file_).name.split(".")[0]
+                localdf_full = localdf.copy()
+                # pdf, vdf = yf_chart(localdf, fig_size=(16, 8), stats=False, linreg=False)
+                # st.pyplot(pdf.figure)
+                # if vdf is not None: st.pyplot(vdf.figure)
+                _success(f"{localdf.name} Ready")
+
+                if st.checkbox(f"Show DataFrame: {localdf.name} | {localdf.shape}", False, key="show_localdf"): st.dataframe(localdf_full)
+
+
+def localfile_selector(column=None, title="### Upload"):
+    column = _is_st_column(column)
+    column.markdown(title)
+    # localfile_data()
+    with st.container():
+        file = st.file_uploader("Choose a file", type=["csv", "json"], key="uploaded_file")
+        if file is not None:
+            file_suffix = file.name.split(".")[1]
+            # fd = file_.read()
+            # st.write([x for x in dir(file_) if not x.startswith('_')])
+            _success(f"{file.name} Ready")
+            # ready = st.success(f"{file.name} Ready")
+            # tsleep(1.2)
+            # ready.empty()
+
+            if "local_file_name" not in st.session_state["pta"]:
+                st.session_state["pta"]["local_file_name"] = file.name
+
+            if file_suffix == "csv":
+                localdf = pd.read_csv(file)
+            if file_suffix == "json":
+                localdf = pd.read_json(file)
+
+            if "localdf" not in st.session_state["pta"]:
+                st.session_state["pta"]["localdf"] = localdf
+
+            if not localdf.empty:
+                st.markdown("### Chart")
+                # st.markdown(st.session_state["pta"]["localdf"])
+                # st.markdown(type(st.session_state["pta"]["localdf"]))
+
+                if st.checkbox(f"Show DataFrame: {file.name} | {localdf.shape}", False, key="show_localdf"): st.dataframe(localdf)
+        else:
+            if "local_file_name" not in st.session_state["pta"]:
+                st.session_state["pta"]["local_file_name"] = None
+            # st.write(f"file: {file}")
+
+
+
+def return_file(path, ftype:str="json"):
+    result, p = [], Path(path)
+    for item in p.iterdir():
+        if item.is_file() and item.suffix == f".{ftype}":
+            result.append(item)
+
+    if len(result) == 0: result.append("")
+    return sorted(result)
+
+
+def sample_chart(df:pd.DataFrame, fig_size=(16, 5), colors:list = None, stats:bool = False, idx:int = None, linreg:bool = None):
+    if df.empty: return
+    idx = int(idx) if idx is not None and idx >= 0 else None
+    linreg = bool(linreg) if linreg is not None and isinstance(linreg, bool) else False
+
+    title_ = f"{df.name}" if idx is None else f"{idx}: {df.name}"
+
+    if colors is not None and isinstance(colors, list) and len(colors):
+        colors = ["black", "gray", "green", "lightgreen", "pink", "red", "orange", "orange"]
+    else:
+        colors = None
+
+    if stats:
+        min_, max_ = np.min(df.values), np.max(df.values)
+        mu_, std_ = np.mean(df.values), np.std(df.values)
+        df["mu"] = mu_
+        df["mu-2std"] = mu_ - 2 * std_
+        df["mu-std"] = mu_ - std_
+        df["mu+std"] = mu_ + std_
+        df["mu+2std"] = mu_ + 2 * std_
+        df["min"] = min_
+        df["max"] = max_
+
+        if linreg:
+            df.ta.linreg(close=df.iloc[:,0], length=df.shape[0], fill_method="bfill", append=True)
+            if colors is not None: colors.append("magenta")
+
+    if colors is not None:
+        fig = df.plot(figsize=fig_size, title=title_, color=colors, grid=True, legend=False if len(df.columns) > 10 else True)
+    else:
+        fig = df.plot(figsize=fig_size, title=title_, grid=True, legend=False if len(df.columns) > 10 else True)
+    return fig
+
+
+
+def stochastic_data(title="### Stochastic Options"):
+    st.markdown("###")
+    st.markdown(title)
+    base = st.container()
+    # if st.session_state["sb_stochastic_help"]:
+        # st.write("HELP")
+
+    with base:
+        with st.form("stochastic_params"):
+            stoch_parameters = st.container()
+            with stoch_parameters:
+                st.markdown("#### Parameters")
+                stoch_s11, stoch_s12, stoch_s13 = st.columns(3)
+                sp_ticker = stoch_s11.text_input("Ticker", key="stochastic_ticker")
+                sp_process = stoch_s12.selectbox("Process", ta_sample_processes())
+                sp_noise = stoch_s13.selectbox("Noise", ta_sample_noises())
+
+                stoch_s21, stoch_s22, stoch_s23 = st.columns(3)
+                sp_s0 = stoch_s21.number_input("Initial", value=42.00, min_value=0.00001, step=1e-6, format="%.5f")
+                sp_length = stoch_s22.number_input("Length", value=50, min_value=2, step=1)
+                sp_random_number = stoch_s23.number_input("Random # (0 is random)", value=10, min_value=0, step=1)
+
+                stoch_s31, stoch_s32, stoch_s33 = st.columns(3)
+                sp_future = stoch_s31.checkbox("Future", False)
+                sp_positive = stoch_s32.checkbox("Positive", False)
+                sp_stats = stoch_s33.checkbox("Stats", False, key="sp_stats")
+
+            stoch_addparameters = st.container()
+            with stoch_addparameters:
+                st.markdown("#### Additional Parameters")
+                stoch_s41, stoch_s42, stoch_s43, stoch_s44 = st.columns(4)
+                sp_speed = stoch_s41.number_input("Speed", value=1.00, step=1e-6, format="%.5f")
+                sp_hurst = stoch_s42.number_input("Hurst", value=0.5, min_value=0.0, max_value=1.0, step=1e-6, format="%.5f")
+                sp_scale = stoch_s43.number_input("Scale", value=1.00, step=1e-6, format="%.5f")
+                sp_freq = stoch_s44.selectbox("Date Freq", ["D", "B", "W", "M", "H", "BH", "T", "S", "L", "U", "N", "MS", "BMS", "SM", "SMS", "Q", "QS", "A", "AS", "BA", "BAS"])
+
+                stoch_s51, stoch_s52, stoch_s53, stoch_s54 = st.columns(4)
+                sp_drift = stoch_s51.number_input("Drift", value=0.00, step=1e-6, format="%.5f")
+                sp_volatility = stoch_s52.number_input("Volatility", value=1.00, step=1e-6, format="%.5f")
+                sp_b = stoch_s53.number_input("b", value=0.01, step=1e-6, format="%.5f")
+                sp_t = stoch_s54.number_input("t", value=1.00, step=1e-6, format="%.5f")
+
+            sample_params = {
+                "name": None if len(sp_ticker) == 0 else sp_ticker.upper(),
+                "s0": sp_s0, "length": sp_length,
+                "process": sp_process, "noise": sp_noise, "scale": sp_scale,
+                "drift": sp_drift, "volatility": sp_volatility,
+                "b": sp_b, "t": sp_t, "speed": sp_speed, "hurst": sp_hurst,
+                "future": sp_future, "positive": sp_positive,
+                "random_number": sp_random_number if sp_random_number > 0 else None,
+                "freq": sp_freq
+            }
+
+            mc_parameters = st.container()
+            with mc_parameters:
+                st.markdown("#### Montecarlo Simulation Parameters")
+                # montecarlo = st.checkbox("Simulate", False, key="stoch_montecarlo") # False
+                # if montecarlo:
+                stoch_s61, stoch_s62, stoch_s63, stoch_s64 = st.columns(4)
+                sp_mc_count = stoch_s61.number_input("Simulations", value=5, min_value=2, max_value=10_000, step=1)
+                sp_mc_return_type = stoch_s62.selectbox("Return", ["percent", "log"])
+                sp_mc_selection = stoch_s63.selectbox("Selection", ["permutation", "shuffle", "both"])
+                sp_mc_range = stoch_s64.slider("MC Columns", value=(0, sp_mc_count), min_value=0, max_value=sp_mc_count)
+
+            st.markdown("####")
+            submitted = st.form_submit_button("Submit")
+
+        st.markdown("###")
+        st.markdown("submitted")
+        st.write(submitted)
+        # samplenp = ta.sample(**sample_params).np
+
+        df = ta.sample(**sample_params).df if submitted else ta.df
+
+        if "sampledf" not in st.session_state["pta"]:
+            st.session_state["pta"]["sampledf"] = df
+
+        if not df.empty:
+            # st.markdown(f"### Chart {sampledf.name}")
+            st.markdown(f"#### {df.name}")
+            if st.checkbox(f"Show Chart", True, key="show_sampledf"):
+                sample_fig = sample_chart(df, fig_size=(stoch_plot_width, stoch_plot_height), colors=None, stats=sp_stats).figure
+                st.pyplot(sample_fig)
+            _success(f"Sample Ready")
+            # ready = st.success("Sample Ready")
+            # tsleep(1.2)
+            # ready.empty()
+
+            if st.checkbox(f"Show DataFrame", True, key="show_sampledf"): st.dataframe(df)
+
+        mcdf = pd.DataFrame()
+        # if montecarlo:
+        mcdf = df.copy()
+        # mcdf = pd.DataFrame(np.tile(sampledf.values, sp_mc_count))
+        if sp_mc_return_type == "percent":
+            mcdf_returns = mcdf.ta.percent_return(cumulative=False, fillna=0, append=True)
+        else:
+            mcdf_returns = mcdf.ta.log_return(cumulative=False, fillna=0, append=True)
+
+        return_tilesdf = pd.DataFrame(np.tile(mcdf_returns.values, (sp_mc_count, 1))).T
+
+        if sp_mc_selection == "permutation":
+            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(permute_col)
+        if sp_mc_selection == "shuffle":
+            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(shuffle_col)
+        if sp_mc_selection == "both":
+            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(permute_col)
+            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(shuffle_col)
+
+        pricesdf = sp_s0 * (1 + return_tilesdf).cumprod()
+
+        mcdf = pricesdf.copy()
+        mcdf.index = df.index
+        mcdf = mcdf.iloc[:,sp_mc_range[0]:sp_mc_range[1]]
+        # df.name = "YOU" if len(sp_ticker) == 0 else sp_ticker.upper()
+        # df.name = sp_ticker.upper()
+        # mcdf.name = f"MC{sp_mc_count} - {df.name}"
+        mcdf.name = df.name
+
+        if not mcdf.empty:
+            # st.markdown(f"### Chart {mcdf.name}")
+            sim_statsdf = mcdf.describe(include="all").T
+            sim_mean = sim_statsdf["mean"].mean()
+            sim_std = sim_statsdf["std"].mean()
+
+            st.markdown("---")
+            st.markdown(f"#### {mcdf.name}")
+            st.markdown(f"**{sp_mc_return_type.title()} Return Simulations**: {sp_mc_count}, **Mean**: {round(sim_mean, 5)}, **Stdev**: {round(sim_std, 5)}")
+
+            if st.checkbox(f"Show Chart", True, key="show_mcdf_chart"):
+                mc_fig = sample_chart(mcdf, fig_size=(stoch_plot_width, stoch_plot_height), colors=None).figure
+                st.pyplot(mc_fig)
+            _success(f"MC Ready")
+            # ready = st.success("MC Ready")
+            # tsleep(1.2)
+            # ready.empty()
+
+            # if not st.checkbox(f"MC Simulations Minimized", True, key="show_mcdf"): st.dataframe(mcdf)
+            if st.checkbox(f"Show DataFrame", True, key="show_mcdf_df"):
+                st.markdown("###")
+                st.dataframe(mcdf)
+                st.markdown("###")
+                # st.dataframe(mcdf.cumsum(axis=0))
+                # st.markdown("###")
+                # st.dataframe(mcdf.describe(include="all"))
+                # st.markdown("###")
+                # st.dataframe()
+
+
+def stochastic_selector(column=None, title="### Stochastic Processes"):
+    column = _is_st_column(column)
+    _help_txt = "help(ta.sample)"
+    column.markdown(title)
+    sandbox = column.checkbox("Options", False, "stochastic_sandbox")
+    if sandbox: stochastic_data()
+    expand_stochastic = column.checkbox(_help_txt, False, "expand_stochastic")
+    if expand_stochastic: st.help(ta.sample)
+
+
+def ta_sample_noises():
+    return ta.sample().noises
+
+def ta_sample_processes():
+    return ta.sample().processes
+
+
+def yf_chart(df:pd.DataFrame, fig_size=(16, 10), stats:bool = False, linreg:bool = None):
+    if df.empty: return
+    linreg = bool(linreg) if linreg is not None and isinstance(linreg, bool) else False
+
+    title_ = f"{df.name}" if hasattr(df, "name") else "Local"
+    colors = ["black", "gray", "green", "lightgreen", "pink", "red", "orange", "orange"]
+
+    volumedf = pd.DataFrame()
+    if "volume" in df.columns:
+        volumedf = pd.DataFrame(df["volume"])
+        del df["volume"]
+
+    if stats:
+        min_, max_ = np.min(df.values), np.max(df.values)
+        mu_, std_ = np.mean(df.values), np.std(df.values)
+        df["mu"] = mu_
+        df["mu-2std"] = mu_ - 2 * std_
+        df["mu-std"] = mu_ - std_
+        df["mu+std"] = mu_ + std_
+        df["mu+2std"] = mu_ + 2 * std_
+        df["min"] = min_
+        df["max"] = max_
+
+        if linreg:
+            df.ta.linreg(close=df.iloc[:,0], length=df.shape[0], fill_method="bfill", append=True)
+            colors.append("magenta")
+
+    if "open" in df.columns: del df["open"]
+    if "high" in df.columns: del df["high"]
+    if "low" in df.columns: del df["low"]
+
+    price_fig = df.plot(figsize=fig_size, title=title_, color=colors, grid=True)
+
+    if volumedf.empty:
+        volume_fig = None
+    else:
+        volume_fig = volumedf.plot(figsize=(fig_size[0], 2.5), kind="area", stacked=False, title=f"{title_} Volume", color=["gray"], alpha=0.6, grid=True)
+    return price_fig, volume_fig
+
+
+def yfinance_data(title="### Yahoo Finance Sandbox"):
+    st.markdown(title)
+    base = st.container()
+    with base:
+        stoch_parameters = st.container()
+        with stoch_parameters:
+            st.markdown("#### Parameters")
+            with st.form("yfinance_form"):
+
+                yf_p11, yf_p12, yf_p13 = st.columns(3)
+                yf_ticker = yf_p11.text_input("Ticker", "SPY", key="yf_ticker")
+
+                yf_period = yf_p12.selectbox("Period", "1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max".split(","))
+                yf_interval = yf_p13.selectbox("Interval", "1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo".split(","))
+
+                yf_p21, yf_p22, yf_p23 = st.columns(3)
+                yf_dividends = yf_p21.checkbox("Dividends", False)
+                yf_stocksplits = yf_p22.checkbox("Stock Splits", False)
+                yf_stats = yf_p23.checkbox("Stats", False, key="yf_stats")
+
+                yf_params = {
+                    "ticker": "SPY" if len(yf_ticker) == 0 else yf_ticker.upper(),
+                    "period": yf_period,
+                    "interval": yf_interval,
+                    "lc_cols": True
+                }
+
+                yf_submitted = st.form_submit_button("Submit")
+
+            yfdf = ta.df
+            if yf_submitted:
+                yfdf = ta.df.ta.ticker(**yf_params)
+
+                _drop_cols = []
+                if not yf_dividends: _drop_cols.append("dividends")
+                if not yf_stocksplits: _drop_cols.append("stock splits")
+                yfdf.drop(columns=_drop_cols, errors="ignore", inplace=True)
+
+                if "yfdf" not in st.session_state["pta"]:
+                    st.session_state["pta"]["yfdf"] = yfdf
+
+                if not yfdf.empty:
+                    st.markdown("### Chart")
+                    yfdf_full = yfdf.copy()
+
+                    _success(f"{yf_ticker.upper()} Ready")
+                    # ready = st.success(f"{yf_ticker.upper()} YF Data Ready")
+                    # tsleep(1.25)
+                    # ready.empty()
+                    # pdf, vdf = yf_chart(yfdf, fig_size=(16, 8), stats=yf_stats, linreg=True)
+                    # st.pyplot(pdf.figure)
+                    # st.pyplot(vdf.figure)
+
+def yfinance_selector(column=None, title="### Yahoo Finance"):
+    column = _is_st_column(column)
+    _help_txt = "help(ta.yf)"
+    column.markdown(title)
+    sandbox = column.checkbox("Options", False, "yfinance_options")
+    if sandbox: yfinance_data()
+    expand_yfinance = column.checkbox(_help_txt, False, "expand_yfinance")
+    if expand_yfinance: st.help(ta.yf)
+
+
+### Streamlit Sections
+
+
+def description():
+    expander = st.expander("", True)
+    with expander:
+        bcol1, logocol, bcol2 = st.columns(3)
+        with bcol1: st.write("")
+        with logocol: st.image("https://raw.githubusercontent.com/twopirllc/pandas-ta/main/images/logo.png")
+        with bcol2: st.write("")
+
+        st.markdown("""
+        # Pandas TA
+        ## A Technical Analysis Library in Python 3
+        ####""")
+
+        st.markdown("""
+        [![license](https://img.shields.io/github/license/twopirllc/pandas-ta)](#license)
+        [![Python Version](https://img.shields.io/pypi/pyversions/pandas-ta?style=flat)](https://pypi.org/project/pandas_ta/)
+        [![PyPi Version](https://img.shields.io/pypi/v/pandas-ta?style=flat)](https://pypi.org/project/pandas_ta/)
+        [![Package Status](https://img.shields.io/pypi/status/pandas-ta?style=flat)](https://pypi.org/project/pandas_ta/)
+        [![Downloads](https://img.shields.io/pypi/dm/pandas_ta?style=flat)](https://pypistats.org/packages/pandas_ta)
+        [![Stars](https://img.shields.io/github/stars/twopirllc/pandas-ta?style=flat)](#stars)
+        [![Forks](https://img.shields.io/github/forks/twopirllc/pandas-ta?style=flat)](#forks)
+        [![Used By](https://img.shields.io/badge/used_by-184-orange.svg?style=flat)](#usedby)
+        [![Contributors](https://img.shields.io/github/contributors/twopirllc/pandas-ta?style=flat)](#contributors)
+        [![Issues](https://img.shields.io/github/issues-raw/twopirllc/pandas-ta?style=flat)](#issues)
+        [![Closed Issues](https://img.shields.io/github/issues-closed-raw/twopirllc/pandas-ta?style=flat)](#closed-issues)
+        [![Buy Me a Coffee](https://img.shields.io/badge/buy_me_a_coffee-orange.svg?style=flat)](https://www.buymeacoffee.com/twopirllc)
+
+        ###
+        _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that leverages the Pandas package with more than 140 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: _Candle Pattern_(**cdl_pattern**), _Simple Moving Average_ (**sma**) _Moving Average Convergence Divergence_ (**macd**), _Hull Exponential Moving Average_ (**hma**), _Bollinger Bands_ (**bbands**), _On-Balance Volume_ (**obv**), _Aroon & Aroon Oscillator_ (**aroon**), _Squeeze_ (**squeeze**) and **_many more_**.
+
+        ###
+        ### Example Streamlit Application
+        **This** is a Basic [Streamlit](https://streamlit.io/) Application using the following packages: ```matplotlib```, ```numpy```, ```pandas```, ```pandas_ta```, ```stochastic```, ```streamlit```, ```yfinance```. It is currently not optimized but Pull Requests are open for those that want to help make it better. :sunglasses: It's purpose is to help the user easily find help on indicators and functions as well as allow them to download or generate data for analysis. While this has been initially built with ```matplotlib``` for charting, feel free to modify and include your charts that [Streamlit Charts](https://docs.streamlit.io/library/api-reference/charts) supports.
+
+        To get the most of this App, install:
+        """)
+        st.code("(env) $ pip install matplotlib numpy pandas pandas_ta stochastic streamlit yfinance", "shell")
+
+        st.markdown("""Run the App and check your browser tab for the application.""")
+        st.code("(env) $ streamlit run pta_streamlit.py", "shell")
+        st.markdown("""
+        ####
+        ### For more information
+        See the **Pandas TA** [README](https://github.com/twopirllc/pandas-ta) for more details on basic usage and the [Example Notebooks](https://github.com/twopirllc/pandas-ta/tree/main/examples) for detailed usage.
+        """)
+
+
+def installation(title="**Installation**"):
+    expander = st.expander("", True)
+    with expander:
+        st.markdown(f"""
+        {title}
+        ===================
+
+        Stable
+        ------
+        The ```pip``` version is the last stable release. Version: *0.3.14b*""")
+        st.code("(env) $ pip install pandas_ta", "shell")
+
+        st.markdown("""
+        Latest Version
+        --------------
+        Best choice! Version: *0.3.32b*
+        """)
+        st.code("(env) $ pip install -U git+https://github.com/twopirllc/pandas-ta", "shell")
+
+        st.markdown("""
+        Cutting Edge
+        ------------
+        This is the _Development Version_ which could have bugs and other undesireable side effects. Use at own risk!
+        """)
+        st.code("(env) $ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development", "shell")
+
+
+def tahelp(title="Help"):
+    expander = st.expander("", True)
+    with expander:
+        st.markdown(f"""
+        ## {title}
+        """)
+        st.code("""
+import pandas as pd
+import pandas_ta as ta
+
+# Create a DataFrame so 'ta' can be used.
+df = pd.DataFrame()
+
+# Help about this, 'ta', extension
+help(df.ta)
+
+# List of all indicators
+df.ta.indicators()
+
+# Help about an indicator such as bbands
+help(ta.bbands)
+        """)
+
+        st.markdown("####")
+        st.markdown("""### Categories""")
+        all_tai = [""]
+        all_tai += e.ta.indicators(as_list=True, exclude=exclude_indicators())
+        ind_help = st.selectbox(f"Indicators [{len(all_tai) - 1}]", all_tai)
+        expand_indicators = st.checkbox("Expand", True, "expand_indicators")
+        if expand_indicators and hasattr(ta, ind_help):
+            st.help(getattr(ta, ind_help))
+
+        st.markdown("####")
+        other = [
+            "", "category_files", "combination", "df_dates", "df_month_to_date",
+            "df_quarter_to_date", "df_year_to_date", "erf", "geometric_mean",
+            "get_drift", "get_offset", "hpoly", "inv_norm",
+            "is_datetime_ordered", "linear_regression", "log_geometric_mean",
+            "non_zero_range", "pascals_triangle", "sample", "signed_series",
+            "symmetric_triangle", "tal_ma", "to_utc", "unsigned_differences",
+            "verify_series", "yf", "zero"
+        ]
+        other_help = st.selectbox(f"Other [{len(other) - 1}]", other)
+        expand_other = st.checkbox("Expand", True, "expand_other")
+        if expand_other and hasattr(ta, other_help):
+            st.help(getattr(ta, other_help))
+
+
+def data(title="## Data"):
+
+    expander = st.expander("", True)
+    with expander:
+        st.markdown(title)
+        st.markdown("""**Pandas TA** includes wrappers around ```stochastic```, to generate sample realizations,
+        or ```yfinance```, download US Market data from Yahoo Finance. If you have local _csv_ or _json_ files, you can import them from the left sidebar under "Local".""")
+
+        # st.write(locals())
+        # st.write(st.session_state)
+        if df is not None:# and not df.empty:
+            st.markdown("### Local File")
+            st.markdown("###")
+            st.markdown(f"**Shape**: {df.shape}")
+            # st.markdown(f"**Index**: {', '.join(df.index)}")
+            st.markdown(f"**Index**: {df.index}, **Datetime Ordered**: {ta.is_datetime_ordered(df)}")
+            st.markdown(f"**Columns**: {', '.join(df.columns)}")
+            if st.checkbox("Set Index", True, key="df_set_index"):
+                index_col = st.selectbox("Index by:", [""] + list(df.columns))
+                st.markdown(f"### index_col: {index_col}")
+                if index_col:
+                    df.set_index(pd.DatetimeIndex(df[index_col]), inplace=True, drop=True)
+                    df.drop(index_col, axis=1, inplace=True)
+
+            st.markdown(f"### {df.name} Details")
+            df_detail_11, df_detail_12 = st.columns(2)
+            df_detail_11.markdown(f"**Start**: {df.index[0]}")
+            df_detail_12.markdown(f"**End**: {df.index[-1]}")
+
+                    # df.set_index(pd.DatetimeIndex(index_col), drop=True)
+            st.dataframe(df)
+        # col11, col12 = st.columns(2)
+        # if ta.Imports["stochastic"] and ta.Imports["yfinance"]:
+        #     stochastic_selector(col11)
+        #     yfinance_selector(col12)
+        # elif ta.Imports["stochastic"]:
+        #     stochastic_selector(col11)
+        #     with col12.container():
+        #         st.markdown("#### For Yahoo Finance Data")
+        #         st.code("$ pip install yfinance", "shell")
+        # elif ta.Imports["yfinance"]:
+        #     yfinance_selector(col11)
+        #     with col12.container():
+        #         st.markdown("#### For Stochastic Data")
+        #         st.code("$ pip install stochastic", "shell")
+        # else: pass
+
+
+def analysis(title="Analysis"):
+    expander = st.expander("", True)
+    with expander:
+        st.markdown(f"""
+        ## {title}
+        Pandas TA has three [Programming Conventions](https://github.com/twopirllc/pandas-ta#programming-conventions) to support your programming "style". Below are examples of each convention with indicators shown on the README.
+        """)
+        st.code("""
+# Standard imports
+import pandas as pd
+import pandas_ta as ta
+        """)
+
+        st.markdown("####")
+        st.markdown("""### Data""")
+        st.markdown("Select one of the data source options from the Data section.")
+
+        df_ = pd.DataFrame()
+        if "sampledf" in st.session_state["pta"]:
+            _success("Using Stochastic Data.")
+            df_ = st.session_state["pta"]["sampledf"]
+        if "localdf" in st.session_state["pta"] and st.session_state["pta"]["local_file_name"] is not None:
+            local_file_name = st.session_state["pta"]["local_file_name"]
+            _success(f"Using local file: {local_file_name}")
+            df_ = st.session_state["pta"]["localdf"]
+        if "yfdf" in st.session_state["pta"]:
+            _success("Using Yahoo Finance Download.")
+            df_ = st.session_state["pta"]["yfdf"]
+        if df_.empty:
+            st.info("No ohlcv data selected from 'Data' Section")
+
+        if not df_.empty:
+            st.markdown("#")
+
+            expand_standard_style = st.checkbox("Show Standard Style", False, "expand_standard_style")
+            if expand_standard_style:
+                st.markdown("##")
+                st.markdown("""## Standard (TA Lib style)""")
+                with st.echo():
+                    df = df_.copy()
+                    sma10 = ta.sma(df["close"], length=10)
+                    donchiandf = ta.donchian(df["high"], df["low"], lower_length=10, upper_length=15)
+                    ema10_ohlc4 = ta.ema(ta.ohlc4(df["open"], df["high"], df["low"], df["close"]), length=10)
+                    # You handle the input/output
+                    sma10
+                    donchiandf
+                    ema10_ohlc4
+
+            expand_df_style = st.checkbox("Show DataFrame Extension Style", False, "expand_df_style")
+            if expand_df_style:
+                st.markdown("##")
+                st.markdown("""### DataFrame Extension""")
+                with st.echo():
+                    df = df_.copy()
+                    sma10 = df.ta.sma(length=10, append=True)
+                    df.ta.ema(close=df.ta.ohlc4(append=True), length=10, suffix="OHLC4", append=True)
+                    donchiandf = df.ta.donchian(lower_length=10, upper_length=15, append=True)
+                    # Append with 'append=True' when using the DataFrame Extension .ta.
+                    df
+                    # As well as Standard Style returns
+                    sma10
+                    donchiandf
+
+            expand_pta_style = st.checkbox("Show Pandas TA Style", False, "expand_pta_style")
+            if expand_pta_style:
+                st.markdown("##")
+                st.markdown("""### Pandas TA Strategy""")
+                with st.echo():
+                    df = df_.copy()
+                    # (1) Create the Strategy
+                    MyStrategy = ta.Strategy(
+                        name="DCSMA10",
+                        ta=[
+                            {"kind": "ohlc4"},
+                            {"kind": "sma", "length": 10},
+                            {"kind": "donchian", "lower_length": 10, "upper_length": 15},
+                            {"kind": "ema", "close": "OHLC4", "length": 10, "suffix": "OHLC4"},
+                        ]
+                    )
+
+                    # (2) Run the Strategy
+                    df.ta.strategy(MyStrategy)
+                    df
+
+
+def strategy_checker(title="## Strategy Checker"):
+    # expander = st.expander("", True)
+    # with expander:
+    with st.expander("", True):
+        st.markdown(title)
+        st.markdown("""
+        Check your **Pandas TA** Strategy code.
+
+        When testing JSON Strategy code, use array notation and _not_ tuple notation for 'col_names' and 'col_numbers' keywords. The arrays _will_ be converted to tuples in the process.
+        """)
+
+        # strategy = None
+        with st.form(key="json_strategy_checker"):
+            if "strategy_txt" not in st.session_state["pta"]:
+                st.session_state["pta"]["strategy_txt"] = strategy_sample
+
+            json_strategy_text = st.text_area("Paste JSON below.", height=300, value=st.session_state["pta"]["strategy_txt"])
+            submit_json_strategy_check = st.form_submit_button("Check")
+            if submit_json_strategy_check:
+                strategy = check_strategy(json_strategy_text, False)
+                if strategy is not None and not isinstance(strategy, bool):
+                    _success(f"{strategy.name} is valid.")
+
+                    strat_check_col1, strat_check_col2, strat_check_col3, strat_check_col4 = st.columns(4)
+                    strat_check_col1.markdown("**Name**")
+                    strat_check_col2.write(strategy.name)
+                    if hasattr(strategy, "description") and len(strategy.description):
+                        strat_check_col3.markdown("**Description**")
+                        strat_check_col4.write(strategy.description)
+                    st.markdown(f"**TA[{len(strategy.ta)}]**")
+                    st.json(strategy.ta)
+
+                    # if "strategy_txt" not in st.session_state["pta"]:
+                    st.session_state["pta"]["strategy_txt"] = json_strategy_text
+
+
+                else:
+                    st.warning("Not a valid Pandas TA JSON Strategy")
+
+
+def chart(title="Charting"):
+    st.markdown(f"""
+    ## {title}
+    """)
+
+
+def disclaimer(title="## Disclaimer"):
+    st.markdown(f"""
+    {title}
+
+    ###### All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
+
+    ###### Any opinions, news, research, analyses, prices, or other information offered is provided as general market commentary, and does not constitute investment advice. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of or reliance on such information.
+    """)
+
+
+def sidebar(title="Site Options"):
+    st.sidebar.markdown(f"# {title}")
+
+    st.sidebar.container()
+    container = st.sidebar.container()
+    # with container:
+
+        # st.sidebar.checkbox("Show Installation", True)
+        # st.sidebar.checkbox("Show Disclaimer", True)
+        # show_installation = st.sidebar.checkbox("Show Installation", True)
+        # show_disclaimer = st.sidebar.checkbox("Show Disclaimer", True)
+    return st
+
+def sidebar_data_options(title="## Data"):
+    with st.sidebar.expander("Options", True):
+        sources = ["Local"]
+        if ta.Imports["stochastic"]: sources.append("Stochastic")
+        if ta.Imports["yfinance"]: sources.append("Yahoo Finance")
+
+        data_choice = st.radio("Select", sources, key="sb_data_choice")
+
+        if data_choice == "Stochastic":
+            st.checkbox("Options", False, key="sb_stochastic_options")
+            st.checkbox("Help", False, key="sb_stochastic_help")
+
+        if data_choice == "Yahoo Finance":
+            st.checkbox("Options", False, key="sb_yfinance_options")
+            st.checkbox("Help", False, key="sb_yfinance_help")
+
+        if data_choice == "Local":
+            file = st.file_uploader("Choose a file", type=["csv", "json"], key="sb_uploaded_file")
+            if file is not None:
+                # file_suffix = file.name.split(".")[1]
+                _success(f"{file.name} Ready")
+
+                # if "local_file_name" not in st.session_state["pta"]:
+                #     st.session_state["pta"]["local_file_name"] = file.name
+
+                if st.session_state["sb_uploaded_file"] is not None:
+                    df = _load_file(st.session_state["sb_uploaded_file"])
+                    df.name = file.name.split(".")[0]
+
+                # if "localdf" not in st.session_state["pta"]:
+                #     st.session_state["pta"]["localdf"] = localdf
+
+                # if st.session_state["sb_uploaded_file"] is None:
+                #     if "data_local_filename" not in st.session_state:
+                #         st.session_state["data_local_filename"] = file.name
+
+                # st.write(st.session_state.get("data_local_filename"))
+                # st.write(st.session_state.get("sb_uploaded_file"))
+                # if st.checkbox(f"Loaded {file.name} | {localdf.shape}", False, key="show_localdf"):
+                #     st.write("1, 2, 3, ...")
+                return df
+
+            # if file_ is not None:
+            #     file_suffix = file_.name.split(".")[1]
+            #     # fd = file_.read()
+            #     # st.write([x for x in dir(file_) if not x.startswith('_')])
+            #     _success(f"{file_.name} Ready")
+
+            #     if "local_file_name" not in st.session_state["pta"]:
+            #         st.session_state["pta"]["local_file_name"] = file_.name
+
+            #     # st.write(st.session_state)
+
+            #     if file_suffix == "csv":
+            #         localdf = pd.read_csv(file_)
+            #     elif file_suffix == "json":
+            #         localdf = pd.read_json(file_)
+            #     else: pass
+
+            #     if "localdf" not in st.session_state["pta"]:
+            #         st.session_state["pta"]["localdf"] = localdf
+
+            # if not localdf.empty and file is not None:
+            #     st.markdown("### Chart")
+            #     localdf.name = file.name
+            #     # localdf.name = Path(file).name.split(".")[0]
+            #     localdf_full = localdf.copy()
+            #     # pdf, vdf = yf_chart(localdf, fig_size=(16, 8), stats=False, linreg=False)
+            #     # st.pyplot(pdf.figure)
+            #     # if vdf is not None: st.pyplot(vdf.figure)
+            #     _success(f"{localdf.name} Ready")
+
+
+if __name__ == "__main__":
+    st.set_page_config(
+        "Pandas TA Help and Examples",
+        page_icon=":bar-chart:",
+        initial_sidebar_state="expanded",
+        layout="wide"
+    )
+    # st.sidebar.markdown(f"**Date**: {dt_today.strftime('%Y-%m-%d')}")
+    show = {}
+
+    # Sidebar
+    page_options = st.sidebar.container()
+    with page_options:
+        st.sidebar.markdown("# Sections")
+        show["description"] = st.sidebar.checkbox("Description", False, key="sb_description_check")
+        show["installation"] = st.sidebar.checkbox("Installation", False, key="sb_installation_check")
+        show["help"] = st.sidebar.checkbox("Help", False, key="sb_help_check")
+
+        show["data"] = st.sidebar.checkbox("Data", True, key="sb_data_check")
+        df = sidebar_data_options() if show["data"] else None
+
+        show["analysis"] = st.sidebar.checkbox("Analysis", False, key="sb_analysis_check")
+        show["strategy_checker"] = st.sidebar.checkbox("Strategy Checker", False, key="sb_strategy_check")
+        show["chart"] = st.sidebar.checkbox("Charting", False, key="sb_chart_check")
+        show["disclaimer"] = st.sidebar.checkbox("Disclaimer", False, key="sb_disclaimer_check")
+
+    st.sidebar.markdown("---")
+    st.sidebar.markdown("# Settings")
+    show["display"] = st.sidebar.checkbox("Display", True, key="sb_display_check")
+    # width_slider = st.sidebar.slider("Width Percent", value=95, min_value=85, max_value=100)
+    # _max_width_(pct_width=width_slider)
+    if show["display"]:
+        sb_plot_expander = st.sidebar.expander("Stochastic Plot Size", False)
+        with sb_plot_expander:
+            stoch_plot_width = st.number_input("Width", value=float(16), min_value=float(1), key="sb_stoch_plot_width")
+            stoch_plot_height = st.number_input("Height", value=float(8), min_value=float(1), key="sb_stoch_plot_height")
+
+    # Main Page
+    if show["description"]: description()
+    if show["installation"]: installation()
+    if show["help"]: tahelp()
+    if show["data"]:
+        data()
+        # Options for Stoch and YF here
+        def _sb_stochastic_options():
+            if "sb_stochastic_options" in st.session_state and st.session_state["sb_stochastic_options"]:
+                stochastic_data()
+        def _sb_stochastic_help():
+            if "sb_stochastic_help" in st.session_state and st.session_state["sb_stochastic_help"]:
+                st.markdown("---")
+                st.markdown("#### Stochastic Help")
+                st.help(ta.sample)
+
+        def _sb_yfinance_options():
+            if "sb_yfinance_options" in st.session_state and st.session_state["sb_yfinance_options"]:
+                st.markdown("---")
+                st.markdown("### Yahoo Finance Options")
+                yfinance_data()
+
+                if not st.session_state["yfdf"].empty:
+                    st.dataframe(st.session_state["yfdf"])
+                    # st.dataframe(yfdf_full)
+                    # if st.checkbox(f"Show DataFrame: {yf_ticker.upper()} | {yfdf.shape}", False, key="show_yfdf"):
+                        # st.dataframe(yfdf_full)
+
+        def _sb_yfinance_help():
+            if "sb_yfinance_help" in st.session_state and st.session_state["sb_yfinance_help"]:
+                st.markdown("---")
+                st.markdown("#### Yahoo Finance Help")
+                st.help(ta.yf)
+
+        _sb_stochastic_options()
+        _sb_stochastic_help()
+
+        _sb_yfinance_options()
+        _sb_yfinance_help()
+    if show["analysis"]: analysis()
+    if show["strategy_checker"]: strategy_checker()
+    if show["chart"]: chart()
+    if show["disclaimer"]: disclaimer()
+    # imports()
+
+st.markdown("###")
+st.markdown("---")
+st.write("st.session_state")
+st.write(st.session_state)
diff --git a/jnb/sample.json b/jnb/sample.json
new file mode 100644
index 0000000..1399672
--- /dev/null
+++ b/jnb/sample.json
@@ -0,0 +1,20 @@
+{
+    "name": "Sample JSON Strategy",
+    "description": "Sample JSON Strategy Test",
+    "ta": [
+        {"kind": "ema", "length": 10, "sma": false},
+        {"kind": "sma", "length": 50, "talib": false},
+        {
+            "kind": "bbands",
+            "talib": true,
+            "col_numbers": [0, 1, 2],
+            "col_names": ["BBL", "BBM", "BBU"]
+        },
+        {
+            "kind": "atr",
+            "length": 50,
+            "talib": true,
+            "col_names": ["ATR"]
+        }
+    ]
+}
\ No newline at end of file
diff --git a/jnb/simple_strategy.json b/jnb/simple_strategy.json
new file mode 100644
index 0000000..cb308fc
--- /dev/null
+++ b/jnb/simple_strategy.json
@@ -0,0 +1,12 @@
+{
+    "name": "Simple Strategy",
+    "description": "Sample JSON Strategy Test 2",
+    "ta": [
+        {"kind": "ema", "length": 8, "sma": true},
+        {"kind": "ema", "length": 21, "talib": true},
+        {"kind": "sma", "length": 50, "talib": true},
+        {"kind": "sma", "length": 200, "talib": true},
+        {"kind": "rsi", "talib": true},
+        {"kind": "obv"}
+    ]
+}
\ No newline at end of file
diff --git a/jnb/talit.py b/jnb/talit.py
new file mode 100644
index 0000000..6530119
--- /dev/null
+++ b/jnb/talit.py
@@ -0,0 +1,499 @@
+# -*- coding: utf-8 -*-
+import datetime
+# import os
+from json import load as json_load
+from pandas_ta.candles.ha import ha
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+import matplotlib.colors as colors
+from matplotlib import rc
+import mplfinance as mpf
+from tqdm.notebook import trange, tqdm
+import streamlit as st
+
+import pandas_ta as ta
+
+
+dt_today = datetime.datetime.now()
+e = pd.DataFrame()
+
+
+def _max_width_(st, pct_width:int = None):
+    """"https://discuss.streamlit.io/t/custom-render-widths/81/8"""
+    if pct_width is None:
+        max_width_str = f"max-width: 2000px;"
+    else:
+        max_width_str = f"max-width: {pct_width};"
+
+    st.markdown(
+        f"""
+    <style>
+    .reportview-container .main .block-container{{
+        {max_width_str}
+    }}
+    </
+    """, unsafe_allow_html=True)
+
+
+@st.cache
+def app_source():
+    result = ["stochastic"]
+    if ta.Imports["yfinance"]: result.append("yfinance")
+    if ta.Imports["alphaVantage-api"]: result.append("AlphaVantage")
+    return result
+
+
+def asset_class():
+    return ["DEMO", "Equity", "Crypto", "FX"]
+
+
+def imports(title="Available Functionality", columns:int = 4):
+    st.sidebar.markdown(f"""## {title}""")
+    total_items = len(ta.Imports.keys())
+    for i in range(0, len(ta.Imports.keys()) - 1):
+        j = 0
+        for k,v in ta.Imports.items():
+            col = st.columns(total_items // columns)
+            # col[].write(k, v)
+            # col[j].checkbox(k, v)
+            print(i, j, (i + j) % columns, (i + j) % columns < columns, k, v)
+            if (i + j) % columns == columns: break
+            j += 1
+            # st.write((i + j) % columns, k, v)
+            # col.write(k)
+            # j += 1
+            # if j + 1 == total_items: break
+            # col[i % cols].write(k)
+            # st.write(k, v)
+            # st.write(k, v)
+            # col[(i + j) % cols].checkbox(k, v, f"i{i}{j}{cols}")
+            # col[1].checkbox(obj[0], obj[1], f"ic1_{i}")
+            # col[2].checkbox(obj[0], obj[1], f"ic2_{i}")
+            # st.write(i, type(obj))
+            # st.write(i % cols, obj[0], obj[1])
+    print("\n\n")
+
+
+def load_strategy(path:str):
+    strat, json_strat = None, json_load(open(path))
+
+    strat = json_strat[0] if isinstance(json_strat, list) and len(json_strat) == 1 else json_strat
+
+    for indicator in strat["ta"]:
+        if "col_numbers" in indicator and len(indicator["col_numbers"]):
+            indicator["col_numbers"] = tuple(indicator["col_numbers"])
+        if "col_names" in indicator and len(indicator["col_names"]):
+            indicator["col_names"] = tuple(indicator["col_names"])
+
+    if strat is not None and hasattr(strat, "description") and len(strat):
+        return ta.Strategy(name=strat["name"], ta=strat["ta"], description=strat["description"])
+    else:
+        return ta.Strategy(name=strat["name"], ta=strat["ta"])
+
+
+def return_file(path, ftype:str="json"):
+    result, p = [], Path(path)
+    for item in p.iterdir():
+        if item.is_file() and item.suffix == f".{ftype}":
+            result.append(item)
+    if len(result) == 0: result.append("")
+    return result
+
+
+def ta_sample_noises():
+    return ta.sample().noises
+
+def ta_sample_processes():
+    return ta.sample().processes
+
+
+def ticker_df(data_source:list, ticker:str):
+    df = None
+    print(f"[!] source: {data_source}")
+    if isinstance(data_source, str) and len(data_source) > 0:
+        _dsl = data_source.lower()
+        if _dsl == "yfinance":
+            # print(f"[!] source: yf")
+            df = ta.df.ta.ticker(ticker, period="max", interval="1d", lc_cols=True)
+            df.drop(columns=["dividends", "stock splits"], inplace=True)
+        else: #"stochastic"
+            print(f"[!] stochastic")
+            # st.sidebar.selectbox("Source", ["stochastic"])
+            # df = ta.sample(s0=np.sample(np.array([1, 2, 5, 10, 13, 25, 42, 50, 68])), process="gbm", length=2 * ta.RATE["TRADING_DAYS_YEAR"], verbose=True).df
+            # print(np.sample(np.array([1, 2, 5, 10, 13, 25, 42, 50, 68])))
+            df = ta.sample(s0=13, process="gbm", noise=None, length=ta.RATE["TRADING_DAYS_PER_YEAR"]).df
+    return df
+
+
+def ticker_list():
+    return ["SPY", "BTC-USD", "ETH-USD", "SPY", "AA", "AAPL", "BAC", "PLTR", "SQ", "TLT", "TWTR", "XLF", "XLK", "OCUL"]
+
+
+def main(*args, **kwargs):
+    # study_path = kwargs.pop("study_path", "sample.json")
+
+    st.markdown("# Pandas TAlit")
+    st.markdown("---")
+
+    # Sidebar
+    st.sidebar.markdown("# OPTIONS")
+    st.sidebar.markdown(f"**Date**: {dt_today.strftime('%Y-%m-%d')}")
+
+    # # selection_col1.markdown("### Selection")
+    # options_expander = st.expander("Options")
+    # oe_col1, oe_col2, oe_col3, oe_col4 = options_expander.columns(4)
+    # # my_expander.write('Hello there!')
+    # # clicked = my_expander.button('Click me!')
+    # # assets_ = my_expander.selectbox("Asset", asset_class())
+
+    # assets_ = oe_col1.selectbox("Asset", asset_class())
+    # source_ = oe_col2.selectbox("Source", app_source())
+    # ticker_ = oe_col3.selectbox("Ticker", ticker_list())
+    # bars_ = oe_col4.selectbox("Last", ["30", "60", "90", "180"])
+
+    # study_expander = st.expander("Studies")
+    # study_col1, study_col2, study_col3 = study_expander.columns(3)
+    # study_path = study_col1.text_input("Directory", ".")
+    # study_submit = study_col3.button("Submit")
+
+    # # 
+    # study_path = return_file(study_path)
+
+    # # study_path = study_col2.selectbox("Study Path", "")
+    # study_ = study_col2.selectbox("File", study_path)
+    # # study = load_strategy(study_path)
+    # print(dir(study_))
+    # print(type(study_))
+    # study = None
+    # if isinstance(study_, Path) and study_.is_file():
+    #     study = load_strategy(study_)
+    #     print(study)
+    # # with selection_col1:
+    #     # assets_ = st.selectbox("Asset", asset_class())
+    #     # source_ = st.selectbox("Source", ["stochastic", "yfinance", "AlphaVantage"])
+    #     # ticker_ = st.selectbox("Ticker", ticker_list())
+    #     # bars_ = st.selectbox("Last", ["30", "60", "90", "180"])
+
+    # # srctkr_options_container = st.container()
+    # # with srctkr_options_container:
+    #     # st.markdown("# DATA")
+    #     # assets_ = st.selectbox("Asset", asset_class())
+    #     # source_ = st.selectbox("Source", ["stochastic", "yfinance", "AlphaVantage"])
+    #     # ticker_ = st.selectbox("Ticker", ticker_list())
+    #     # bars_ = st.selectbox("Last", ["30", "60", "90", "180"])
+
+
+    sb_container = st.sidebar.container()
+    with sb_container:
+        sb_expander = st.expander("Mode")
+        with sb_expander:
+            mode_ = st.radio("Select", ["DEMO", "DATA", "LIVE"])
+
+    with sb_container:
+        sb_expander = st.expander("Studies")
+        with sb_expander:
+            json_path = st.text_input("Directory", ".", key="json_dir")
+            json_path = return_file(json_path, "json")
+            json_file = st.selectbox(f"Files", json_path)
+            try:
+                study_ = load_strategy(json_file)
+                if len(study_.ta): st.text(f"Loaded: {study_.name}")
+            except FileNotFoundError:
+                st.text(f"[X] No JSON Strategy Found")
+
+    sampledf = e.copy()
+    with sb_container:
+        sb_expander = st.expander("Source")
+        with sb_expander:
+            if mode_ == "DATA":
+                source_ = st.radio("Select", [app_source()[0], "csv"])
+
+                # sampledf = e.copy()
+                if source_ == "stochastic":
+                    st.header(source_.title())
+                    source_container = st.container()
+                    with source_container:
+                        sp_name = st.text_input("Ticker")
+                        sp_s0 = st.number_input("Initial", value=42.0, min_value=float(0.01), step=float(0.01))
+                        sp_process = st.selectbox("Process", ta_sample_processes())
+                        sp_noise = st.selectbox("Noise", ta_sample_noises())
+                        sp_length = st.number_input("Length", value=1000, min_value=2, step=1)
+                        sp_random_number = st.number_input("Random # (0 is random)", value=0, min_value=0, step=1)
+                        sp_future = st.checkbox("Future", True)
+                        sp_positive = st.checkbox("Positive", True)
+                        sp_stats = st.checkbox("Stats", False)
+
+                        sample_params = {
+                            "name": None if len(sp_name) == 0 else sp_name,
+                            "s0": sp_s0,
+                            "process": sp_process,
+                            "noise": sp_noise,
+                            "length": sp_length,
+                            "future": sp_future,
+                            "positive": sp_positive,
+                            "random_number": sp_random_number if sp_random_number > 0 else None
+                        }
+                        sampledf = ta.sample(**sample_params).df
+
+            else:
+                source_ = st.radio("Select", app_source()[1:])
+
+    csvdf = e.copy()
+    if source_ == "csv":
+        with sb_container:
+            sb_expander = st.expander("Local")
+            with sb_expander:
+                csv_path = st.text_input("Directory", ".", key="csv_dir")
+                csv_path = return_file(csv_path, "csv")
+                csv_ = st.selectbox("CSV File", csv_path)
+                csvdf = pd.read_csv(csv_)
+                # st.write(csv_path)
+                # st.write(csv_)
+
+    if not csvdf.empty:
+        st.markdown("### Local Data")
+        if st.checkbox("Show Chart", True):
+            sample_colors = ["black", "blue"]
+            if "date" in csvdf.columns: csvdf.set_index("date", inplace=True)
+            if "datetime" in csvdf.columns: csvdf.set_index("datetime", inplace=True)
+            # sample_fig = csvdf.plot(figsize=(16, 10), title=csv_, color=sample_colors, grid=True).figure
+            sample_fig = csvdf["close"].plot(figsize=(16, 10), title=csv_, color=sample_colors, grid=True).figure
+            st.pyplot(sample_fig)
+        if st.checkbox("Show", False): st.dataframe(csvdf)
+
+    # sampledf = e.copy()
+    # if source_ == "stochastic":
+    #     with sb_container:
+    #         sb_expander = st.expander("Parameters")
+    #         with sb_expander:
+    #             sp_name = st.text_input("Ticker")
+    #             sp_s0 = st.number_input("Initial", value=42.0, min_value=float(0.01), step=float(0.01))
+    #             sp_process = st.selectbox("Process", ta_sample_processes())
+    #             sp_noise = st.selectbox("Noise", ta_sample_noises())
+    #             sp_length = st.number_input("Length", value=1000, min_value=2, step=1)
+    #             sp_random_number = st.number_input("Random # (0 is random)", value=0, min_value=0, step=1)
+    #             sp_future = st.checkbox("Future", True)
+    #             sp_positive = st.checkbox("Positive", True)
+    #             sp_stats = st.checkbox("Stats", False)
+
+    #             sample_params = {
+    #                 "name": None if len(sp_name) == 0 else sp_name,
+    #                 "s0": sp_s0,
+    #                 "process": sp_process,
+    #                 "noise": sp_noise,
+    #                 "length": sp_length,
+    #                 "future": sp_future,
+    #                 "positive": sp_positive,
+    #                 "random_number": sp_random_number if sp_random_number > 0 else None
+    #             }
+    #             sampledf = ta.sample(**sample_params).df
+
+    if not sampledf.empty:
+        st.markdown("## Stochastic Sample")
+        sample_colors = ["black", "blue"]
+        sample_fig = sampledf.plot(figsize=(16, 10), title=sampledf.name, color=sample_colors, grid=True).figure
+        st.pyplot(sample_fig)
+
+
+    yfdf = e.copy()
+    if source_ == "yfinance":
+        with sb_container:
+            sb_expander = st.expander("Parameters")
+            with sb_expander:
+                yf_ticker = st.text_input("Ticker", ticker_list()[0])
+                # yf_s0 = st.number_input("Initial", value=42.0, min_value=float(0.01), step=float(0.01))
+                # sp_process = st.selectbox("Process", ta_sample_processes())
+                # sp_noise = st.selectbox("Noise", ta_sample_noises())
+                # sp_length = st.number_input("Length", min_value=2, step=1)
+                yf_period = st.selectbox("Period", "1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max".split(","))
+                if yf_period in "1d,5d,1mo".split(","):
+                    yf_interval = st.selectbox("Interval", "1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo".split(","))
+                # sp_future = st.checkbox("Future", True)
+                # sp_positive = st.checkbox("Positive", True)
+                sp_stats = st.checkbox("Stats", False)
+
+                yf_params = {
+                    "ticker": None if len(yf_ticker) == 0 else yf_ticker,
+                #     "s0": sp_s0,
+                #     "process": sp_process,
+                #     "noise": sp_noise,
+                #     "length": sp_length,
+                #     "future": sp_future,
+                    "period": yf_period,
+                    "interval": yf_interval,
+                    "lc_cols": True
+                }
+                yfdf = ta.yf(**yf_params)
+
+    if not yfdf.empty:
+        st.markdown("## Yahoo Finance ")
+        yf_colors = ["black", "blue"]
+        yf_fig = yfdf.plot(figsize=(16, 10), title=yf_ticker, color=yf_colors, grid=True).figure
+        st.pyplot(yf_fig)
+
+    st.markdown("#")
+    ind_help = None
+    pta_help_expander = st.expander("Pandas TA Indicator Help")
+    with pta_help_expander:
+        all_tai = e.ta.indicators(as_list=True)
+        ind_help = st.selectbox(f"Indicators [{len(all_tai)}]", all_tai)
+        collapse_ta_help = st.checkbox("Collapse", False)
+        if not collapse_ta_help and hasattr(ta, ind_help):
+            st.help(getattr(ta, ind_help))
+
+
+    # with st.sidebar.form(key="Form1"):
+    #     st.header("Options")
+    #     ticker_ = st.text_input("Ticker", "")
+    #     _include_retweets = st.checkbox("Check")
+    #     _num_of_tweets = st.number_input("number input", 100)
+    #     form1_submitted = st.form_submit_button(label="Submit 🔎")
+
+    # date_options_container = st.sidebar.container()
+    # with date_options_container:
+    #     st.markdown("# DATE\n\n")
+        # start_date = st.sidebar.date_input("Start", datetime.date(2019, 1, 1))
+        # end_date = st.sidebar.date_input("End", dt_today)
+
+    # print(type())
+
+    # # dl_state = st.text(f"Loading '{source_}' data...")
+    # df = ticker_df(source_, ticker_)
+    # print(f"df[{type(df)}:{df.shape}]:\n{df}\n")
+    # # print(type(df))
+    # # dl_state = st.text("")
+
+    # if df is not None and study is not None and len(study.ta):
+    #     # dl_state = st.text(f"Running Strategy: {study.name}...")
+    #     df.ta.strategy(study, timed=True, verbose=True)
+    #     # dl_state = st.text("")
+    #     print(df)
+
+    # print(df.shape)
+    # print(ssdf.shape)
+
+    # print(df.iloc[:,:-20])
+    # print(df.tail(int(bars_)))
+    # df = ta.df.ta.ticker(tickerSymbol, period="max")
+    # tickerData = yf.Ticker(tickerSymbol) # Get ticker data
+    # tickerDf = tickerData.history(period='1d', start=start_date, end=end_date) #get the historical prices for this ticker
+    # st.write(tickerSymbol)
+
+    # if not df.empty and df.shape[0] > 0:
+    #     st.write(_data_header + f" - Last {bars_}")
+    #     st.dataframe(df[:-int(bars_)])
+    # else:
+        # st.write(_data_header)
+        # st.dataframe(df)
+
+    # if df is not None:# or not df.empty:
+    #     _df = df.copy()
+    #     _dre, _drs  = _df.index[-1].strftime('%Y-%m-%d'), _df.index[0].strftime('%Y-%m-%d')
+
+    #     _data_header = f"{ticker_}[{source_}| {df.shape}] from {_drs} to {_dre}"
+    #     if "volume" in _df.columns:
+    #         colors = ["black", "blue"]
+    #     else:
+    #         colors = ["black", "green", "red", "violet", "purple", "violet"]
+
+    #     if "volume" in df.columns:
+    #         print(_df.columns)
+    #         voldf = _df["volume"]
+    #         del _df["volume"]
+    #         if "OBV" in df.columns:
+    #             obvdf = _df["volume"]
+    #             del _df["volume"]
+
+    #         _df = _df.iloc[:,3:]
+    #         print(_df.columns)
+
+    #         # fig = _df.plot(figsize=(16, 10), title=_data_header, color=colors, grid=True).figure
+    #         # fig2 = voldf.plot(figsize=(16, 3), kind="bar", stacked=False, title=f"{_data_header} Volume", color=["silver"], grid=True).figure
+    #         fig2 = voldf.plot(figsize=(16, 3), title=f"{_data_header} Volume", color=["gray"], grid=True).figure
+    #         # st.pyplot(fig, clear_figure=False)
+    #         st.pyplot(fig2)
+    #     else:
+    #         fig = _df.plot(figsize=(16, 10), title=_data_header, color=colors, grid=True).figure
+    #         st.pyplot(fig)
+
+    #     # bars_ = 20
+    #     # if df.shape[0] > int(bars_):
+    #     #     st.write(_data_header + f" {bars_} bars")
+    #     #     st.dataframe(df[-int(bars_):], 1000)
+    #     # else:
+    #     #     st.write(_data_header)
+    #     #     st.dataframe(df, 1000)
+
+    # display_options_container = st.container()
+    # with display_options_container:
+    #     if st.checkbox(f"Maximize"):
+    #         _max_width_(st)
+
+    #     if st.checkbox(f"Last {bars_} bars"):
+    #         if df.shape[0] > int(bars_):
+    #             tmpdf = df.copy()
+    #             st.write(_data_header + f" ({bars_} bars)")
+    #             # tmpdf = tmpdf[-int(bars_):]
+    #             # st.dataframe(df[-int(bars_):], 1000)
+    #             st.dataframe(tmpdf, 1000)
+    #         else:
+    #             st.write(_data_header)
+    #             st.dataframe(df, 1000)
+
+
+    # def my_widget(key):
+    #     st.subheader("Hello there!")
+    #     return st.button(f"Click me {key}")
+
+    # # This works in the main area
+    # clicked = my_widget("first")
+
+    # # And within an expander
+    # my_expander = st.expander("Expand", expanded=True)
+    # with my_expander:
+    #     clicked = my_widget("second")
+
+    # if not st.sidebar.checkbox(f"Last {ta.RATE['TRADING_DAYS_PER_YEAR']} bars"):
+    #     st.table(df[:ta.RATE['TRADING_DAYS_PER_YEAR']])
+    # elif st.sidebar.checkbox(f"Last {last_bars} bars"):
+    #     st.table(df[:int(last_bars)])
+
+    # Ticker information
+    # string_logo = '<img src=%s>' % tickerData.info['logo_url']
+    # st.markdown(string_logo, unsafe_allow_html=True)
+
+    # string_name = tickerData.info['longName']
+    # st.header('**%s**' % string_name)
+
+    # string_summary = tickerData.info['longBusinessSummary']
+    # st.info(string_summary)
+
+    # Ticker data
+    # st.header('**Ticker data**')
+    # st.write(tickerDf)
+
+    # Bollinger bands
+    # st.header('**Bollinger Bands**')
+    # qf = cf.QuantFig(tickerDf,title='First Quant Figure',legend='top',name='GS')
+    # qf.add_bollinger_bands()
+    # fig = qf.iplot(asFigure=True)
+    # st.plotly_chart(fig)
+
+    ####
+    #st.write('---')
+    #st.write(tickerData.info)
+
+    # show = {}
+    # if show["data"]:
+    #     sb_data_expander = st.sidebar.expander("Source", True)
+    #     with sb_data_expander:
+    #         # stoch_plot_width = st.number_input("Width", value=float(16), min_value=float(1), key="sb_stoch_plot_width")
+    #         # stoch_plot_height = st.number_input("Height", value=float(8), min_value=float(1), key="sb_stoch_plot_height")
+    #         data_source_ = st.radio("Select", ["local", "stochastic", "yfinance"], key="")
+
+
+if __name__ == "__main__":
+    main()
\ No newline at end of file
diff --git a/jnb/test.json b/jnb/test.json
new file mode 100644
index 0000000..452e349
--- /dev/null
+++ b/jnb/test.json
@@ -0,0 +1,10 @@
+{
+    "name": "Sample JSON Strategy 2",
+    "description": "Sample JSON Strategy Test 2",
+    "ta": [
+        {"kind": "ema", "length": 10, "sma": false},
+        {"kind": "sma", "length": 50, "talib": false},
+        {"kind": "kc", "mamode": "hma"},
+        {"kind": "adx", "length": 10}
+    ]
+}
\ No newline at end of file

From 3486a71192f15d5e37694202b5287485bd0c81c5 Mon Sep 17 00:00:00 2001
From: Kevin Johnson <appliedmathkj@gmail.com>
Date: Wed, 8 Dec 2021 14:42:09 -0800
Subject: [PATCH 2/6] DEV undo

---
 jnb/pta_streamlit.py     | 1076 --------------------------------------
 jnb/sample.json          |   20 -
 jnb/simple_strategy.json |   12 -
 jnb/talit.py             |  499 ------------------
 jnb/test.json            |   10 -
 5 files changed, 1617 deletions(-)
 delete mode 100644 jnb/pta_streamlit.py
 delete mode 100644 jnb/sample.json
 delete mode 100644 jnb/simple_strategy.json
 delete mode 100644 jnb/talit.py
 delete mode 100644 jnb/test.json

diff --git a/jnb/pta_streamlit.py b/jnb/pta_streamlit.py
deleted file mode 100644
index 7234f37..0000000
--- a/jnb/pta_streamlit.py
+++ /dev/null
@@ -1,1076 +0,0 @@
-# -*- coding: utf-8 -*-
-import datetime
-from os import read
-from pathlib import Path
-from json import load as json_load
-from json import loads as json_loads
-from json import dump as json_dump
-from json import dumps as json_dumps
-
-from time import sleep as tsleep
-from matplotlib.pyplot import axis
-
-import numpy as np
-import pandas as pd
-
-# import matplotlib.pyplot as plt
-# import matplotlib.colors as colors
-# from matplotlib import rc
-# import mplfinance as mpf
-# from tqdm.notebook import trange, tqdm
-import streamlit as st
-from streamlit.state.session_state import SessionState
-
-import pandas_ta as ta
-
-
-dt_today = datetime.datetime.now()
-e = pd.DataFrame()
-
-pta_session = {}
-
-strategy_sample = \
-"""{
-    "name": "Sample JSON Strategy",
-    "description": "Sample JSON Strategy Test",
-    "ta": [
-        {"kind": "ema", "length": 10, "sma": false},
-        {"kind": "sma", "length": 50, "talib": false},
-        {
-            "kind": "bbands",
-            "talib": true,
-            "col_numbers": [0, 1, 2],
-            "col_names": ["BBL", "BBM", "BBU"]
-        },
-        {
-            "kind": "atr",
-            "length": 50,
-            "talib": true,
-            "col_names": ["ATR"]
-        }
-    ]
-}"""
-
-if "pta" not in st.session_state:
-    st.session_state["pta"] = pta_session
-
-print(f"[!] st.session_state: {st.session_state}\n")
-
-
-
-def _chart():
-    pass
-
-def _count_down_container(message:str = None, n:int = 1):
-    """https://docs.streamlit.io/library/api-reference/layout/st.empty"""
-    with st.empty():
-        for seconds in range(n):
-            if message is None:
-                st.write(f"⏳ {seconds} seconds have passed")
-            else:
-                st.write(message)
-            tsleep(1)
-
-        if message is None: st.write("✔️ Times up!")
-        st.empty()
-
-
-def _is_st_column(column):
-    if column is None: return st
-    return column if isinstance(column, st._DeltaGenerator) else st
-
-
-@st.cache(ttl=60, show_spinner=True)
-def _load_file(file):
-    """Load file of type st.UploadedFile"""
-    suffix = file.name.split(".")[1]
-    result = None
-    if suffix == "csv":
-        result = pd.read_csv(file)
-    elif suffix == "json":
-        result = pd.read_json(file)
-    elif suffix == "pkl":
-        result = pd.read_pickle(file)
-    else: # Default
-        result = pd.read_csv(file)
-    return result
-
-
-def _max_width_(pct_width:int = None):
-    """"https://discuss.streamlit.io/t/custom-render-widths/81/8"""
-    if pct_width is None:
-        max_width_str = f"max-width: 2000px;"
-    else:
-        max_width_str = f"max-width: {pct_width}%;"
-
-    st.markdown(
-        f"""
-    <style>
-    .reportview-container .main .block-container{{
-        {max_width_str}
-    }}
-    </
-    """, unsafe_allow_html=True)
-
-
-def _success(msg:str = "Success!", secs:float = 1.25):
-    ready = st.success(msg)
-    tsleep(secs)
-    ready.empty()
-
-
-def _talist2tuple(ta_list:list):
-    """Convert "col_numbers" and "col_names" list values to tuples"""
-    for indicator in ta_list:
-        if "col_numbers" in indicator and len(indicator["col_numbers"]):
-            indicator["col_numbers"] = tuple(indicator["col_numbers"])
-        if "col_names" in indicator and len(indicator["col_names"]):
-            indicator["col_names"] = tuple(indicator["col_names"])
-    return ta_list
-
-
-def permute_col(x: pd.Series):
-    """Permute values in a Pandas Series"""
-    return np.random.permutation(x)
-
-def shuffle_col(x: pd.Series, frac:float = 1.0):
-    """Shuffle values and reset index in a Pandas Series"""
-    _ = x.sample(frac=frac)
-    _.index = x.index
-    return _
-
-
-def check_strategy(json_text, as_bool:bool = False):
-    strat = json_loads(json_text)
-    if "name" not in strat or "ta" not in strat: return False
-
-    if isinstance(strat["ta"], list) and len(strat["ta"]) > 0:
-        strat["ta"] = _talist2tuple(strat["ta"])
-    else:
-        return False
-
-    result = None
-    if "description" in strat:
-        result = ta.Strategy(name=strat["name"], ta=strat["ta"], description=strat["description"])
-    else:
-        result = ta.Strategy(name=strat["name"], ta=strat["ta"])
-
-    if as_bool:
-        return True if result is not None else False
-    return result
-
-
-def exclude_indicators():
-    return [
-        "above", "above_value", "below", "below_value", "cross", "cross_value"
-    ]
-
-
-def load_strategy(path:str):
-    strat, json_strat = None, json_load(open(path))
-    strat = json_strat[0] if isinstance(json_strat, list) and len(json_strat) == 1 else json_strat
-
-    for indicator in strat["ta"]:
-        if "col_numbers" in indicator and len(indicator["col_numbers"]):
-            indicator["col_numbers"] = tuple(indicator["col_numbers"])
-        if "col_names" in indicator and len(indicator["col_names"]):
-            indicator["col_names"] = tuple(indicator["col_names"])
-
-    if strat is not None and hasattr(strat, "description") and len(strat):
-        return ta.Strategy(name=strat["name"], ta=strat["ta"], description=strat["description"])
-    else:
-        return ta.Strategy(name=strat["name"], ta=strat["ta"])
-
-
-def localfile_data(title="### Upload"):
-    # st.markdown(title)
-    base = st.container()
-    with base:
-        parameters = st.container()
-        with parameters:
-
-            # st.markdown("#### Parameters")
-
-            # lf_p11, lf_p12, lf_p13 = st.columns(3)
-
-            # fpath = lf_p11.text_input("Directory", ".", key="local_dir")
-            # file_suffix = lf_p12.selectbox(f"File Type", ["csv", "json"])
-            # fpath = return_file(fpath, file_suffix)
-            # file_ = lf_p13.selectbox(f"{file_suffix.upper()} File", fpath)
-
-
-            # uploaded_file = st.file_uploader("Choose a file", type=["csv", "json"], key="uploadfile")
-            # lf_p21, lf_p22 = st.columns(2)
-            localdf = pd.DataFrame()
-            file_ = st.file_uploader("Choose a file", type=["csv", "json"], key="uploaded_file")
-            if file_ is not None:
-                file_suffix = file_.name.split(".")[1]
-                # fd = file_.read()
-                # st.write([x for x in dir(file_) if not x.startswith('_')])
-                _success(f"{file_.name} Ready")
-
-                if "local_file_name" not in st.session_state["pta"]:
-                    st.session_state["pta"]["local_file_name"] = file_.name
-
-                # st.write(st.session_state)
-
-                if file_suffix == "csv":
-                    localdf = pd.read_csv(file_)
-                elif file_suffix == "json":
-                    localdf = pd.read_json(file_)
-                else: pass
-
-                if "localdf" not in st.session_state["pta"]:
-                    st.session_state["pta"]["localdf"] = localdf
-
-            if not localdf.empty:
-                st.markdown("### Chart")
-                localdf.name = Path(file_).name.split(".")[0]
-                localdf_full = localdf.copy()
-                # pdf, vdf = yf_chart(localdf, fig_size=(16, 8), stats=False, linreg=False)
-                # st.pyplot(pdf.figure)
-                # if vdf is not None: st.pyplot(vdf.figure)
-                _success(f"{localdf.name} Ready")
-
-                if st.checkbox(f"Show DataFrame: {localdf.name} | {localdf.shape}", False, key="show_localdf"): st.dataframe(localdf_full)
-
-
-def localfile_selector(column=None, title="### Upload"):
-    column = _is_st_column(column)
-    column.markdown(title)
-    # localfile_data()
-    with st.container():
-        file = st.file_uploader("Choose a file", type=["csv", "json"], key="uploaded_file")
-        if file is not None:
-            file_suffix = file.name.split(".")[1]
-            # fd = file_.read()
-            # st.write([x for x in dir(file_) if not x.startswith('_')])
-            _success(f"{file.name} Ready")
-            # ready = st.success(f"{file.name} Ready")
-            # tsleep(1.2)
-            # ready.empty()
-
-            if "local_file_name" not in st.session_state["pta"]:
-                st.session_state["pta"]["local_file_name"] = file.name
-
-            if file_suffix == "csv":
-                localdf = pd.read_csv(file)
-            if file_suffix == "json":
-                localdf = pd.read_json(file)
-
-            if "localdf" not in st.session_state["pta"]:
-                st.session_state["pta"]["localdf"] = localdf
-
-            if not localdf.empty:
-                st.markdown("### Chart")
-                # st.markdown(st.session_state["pta"]["localdf"])
-                # st.markdown(type(st.session_state["pta"]["localdf"]))
-
-                if st.checkbox(f"Show DataFrame: {file.name} | {localdf.shape}", False, key="show_localdf"): st.dataframe(localdf)
-        else:
-            if "local_file_name" not in st.session_state["pta"]:
-                st.session_state["pta"]["local_file_name"] = None
-            # st.write(f"file: {file}")
-
-
-
-def return_file(path, ftype:str="json"):
-    result, p = [], Path(path)
-    for item in p.iterdir():
-        if item.is_file() and item.suffix == f".{ftype}":
-            result.append(item)
-
-    if len(result) == 0: result.append("")
-    return sorted(result)
-
-
-def sample_chart(df:pd.DataFrame, fig_size=(16, 5), colors:list = None, stats:bool = False, idx:int = None, linreg:bool = None):
-    if df.empty: return
-    idx = int(idx) if idx is not None and idx >= 0 else None
-    linreg = bool(linreg) if linreg is not None and isinstance(linreg, bool) else False
-
-    title_ = f"{df.name}" if idx is None else f"{idx}: {df.name}"
-
-    if colors is not None and isinstance(colors, list) and len(colors):
-        colors = ["black", "gray", "green", "lightgreen", "pink", "red", "orange", "orange"]
-    else:
-        colors = None
-
-    if stats:
-        min_, max_ = np.min(df.values), np.max(df.values)
-        mu_, std_ = np.mean(df.values), np.std(df.values)
-        df["mu"] = mu_
-        df["mu-2std"] = mu_ - 2 * std_
-        df["mu-std"] = mu_ - std_
-        df["mu+std"] = mu_ + std_
-        df["mu+2std"] = mu_ + 2 * std_
-        df["min"] = min_
-        df["max"] = max_
-
-        if linreg:
-            df.ta.linreg(close=df.iloc[:,0], length=df.shape[0], fill_method="bfill", append=True)
-            if colors is not None: colors.append("magenta")
-
-    if colors is not None:
-        fig = df.plot(figsize=fig_size, title=title_, color=colors, grid=True, legend=False if len(df.columns) > 10 else True)
-    else:
-        fig = df.plot(figsize=fig_size, title=title_, grid=True, legend=False if len(df.columns) > 10 else True)
-    return fig
-
-
-
-def stochastic_data(title="### Stochastic Options"):
-    st.markdown("###")
-    st.markdown(title)
-    base = st.container()
-    # if st.session_state["sb_stochastic_help"]:
-        # st.write("HELP")
-
-    with base:
-        with st.form("stochastic_params"):
-            stoch_parameters = st.container()
-            with stoch_parameters:
-                st.markdown("#### Parameters")
-                stoch_s11, stoch_s12, stoch_s13 = st.columns(3)
-                sp_ticker = stoch_s11.text_input("Ticker", key="stochastic_ticker")
-                sp_process = stoch_s12.selectbox("Process", ta_sample_processes())
-                sp_noise = stoch_s13.selectbox("Noise", ta_sample_noises())
-
-                stoch_s21, stoch_s22, stoch_s23 = st.columns(3)
-                sp_s0 = stoch_s21.number_input("Initial", value=42.00, min_value=0.00001, step=1e-6, format="%.5f")
-                sp_length = stoch_s22.number_input("Length", value=50, min_value=2, step=1)
-                sp_random_number = stoch_s23.number_input("Random # (0 is random)", value=10, min_value=0, step=1)
-
-                stoch_s31, stoch_s32, stoch_s33 = st.columns(3)
-                sp_future = stoch_s31.checkbox("Future", False)
-                sp_positive = stoch_s32.checkbox("Positive", False)
-                sp_stats = stoch_s33.checkbox("Stats", False, key="sp_stats")
-
-            stoch_addparameters = st.container()
-            with stoch_addparameters:
-                st.markdown("#### Additional Parameters")
-                stoch_s41, stoch_s42, stoch_s43, stoch_s44 = st.columns(4)
-                sp_speed = stoch_s41.number_input("Speed", value=1.00, step=1e-6, format="%.5f")
-                sp_hurst = stoch_s42.number_input("Hurst", value=0.5, min_value=0.0, max_value=1.0, step=1e-6, format="%.5f")
-                sp_scale = stoch_s43.number_input("Scale", value=1.00, step=1e-6, format="%.5f")
-                sp_freq = stoch_s44.selectbox("Date Freq", ["D", "B", "W", "M", "H", "BH", "T", "S", "L", "U", "N", "MS", "BMS", "SM", "SMS", "Q", "QS", "A", "AS", "BA", "BAS"])
-
-                stoch_s51, stoch_s52, stoch_s53, stoch_s54 = st.columns(4)
-                sp_drift = stoch_s51.number_input("Drift", value=0.00, step=1e-6, format="%.5f")
-                sp_volatility = stoch_s52.number_input("Volatility", value=1.00, step=1e-6, format="%.5f")
-                sp_b = stoch_s53.number_input("b", value=0.01, step=1e-6, format="%.5f")
-                sp_t = stoch_s54.number_input("t", value=1.00, step=1e-6, format="%.5f")
-
-            sample_params = {
-                "name": None if len(sp_ticker) == 0 else sp_ticker.upper(),
-                "s0": sp_s0, "length": sp_length,
-                "process": sp_process, "noise": sp_noise, "scale": sp_scale,
-                "drift": sp_drift, "volatility": sp_volatility,
-                "b": sp_b, "t": sp_t, "speed": sp_speed, "hurst": sp_hurst,
-                "future": sp_future, "positive": sp_positive,
-                "random_number": sp_random_number if sp_random_number > 0 else None,
-                "freq": sp_freq
-            }
-
-            mc_parameters = st.container()
-            with mc_parameters:
-                st.markdown("#### Montecarlo Simulation Parameters")
-                # montecarlo = st.checkbox("Simulate", False, key="stoch_montecarlo") # False
-                # if montecarlo:
-                stoch_s61, stoch_s62, stoch_s63, stoch_s64 = st.columns(4)
-                sp_mc_count = stoch_s61.number_input("Simulations", value=5, min_value=2, max_value=10_000, step=1)
-                sp_mc_return_type = stoch_s62.selectbox("Return", ["percent", "log"])
-                sp_mc_selection = stoch_s63.selectbox("Selection", ["permutation", "shuffle", "both"])
-                sp_mc_range = stoch_s64.slider("MC Columns", value=(0, sp_mc_count), min_value=0, max_value=sp_mc_count)
-
-            st.markdown("####")
-            submitted = st.form_submit_button("Submit")
-
-        st.markdown("###")
-        st.markdown("submitted")
-        st.write(submitted)
-        # samplenp = ta.sample(**sample_params).np
-
-        df = ta.sample(**sample_params).df if submitted else ta.df
-
-        if "sampledf" not in st.session_state["pta"]:
-            st.session_state["pta"]["sampledf"] = df
-
-        if not df.empty:
-            # st.markdown(f"### Chart {sampledf.name}")
-            st.markdown(f"#### {df.name}")
-            if st.checkbox(f"Show Chart", True, key="show_sampledf"):
-                sample_fig = sample_chart(df, fig_size=(stoch_plot_width, stoch_plot_height), colors=None, stats=sp_stats).figure
-                st.pyplot(sample_fig)
-            _success(f"Sample Ready")
-            # ready = st.success("Sample Ready")
-            # tsleep(1.2)
-            # ready.empty()
-
-            if st.checkbox(f"Show DataFrame", True, key="show_sampledf"): st.dataframe(df)
-
-        mcdf = pd.DataFrame()
-        # if montecarlo:
-        mcdf = df.copy()
-        # mcdf = pd.DataFrame(np.tile(sampledf.values, sp_mc_count))
-        if sp_mc_return_type == "percent":
-            mcdf_returns = mcdf.ta.percent_return(cumulative=False, fillna=0, append=True)
-        else:
-            mcdf_returns = mcdf.ta.log_return(cumulative=False, fillna=0, append=True)
-
-        return_tilesdf = pd.DataFrame(np.tile(mcdf_returns.values, (sp_mc_count, 1))).T
-
-        if sp_mc_selection == "permutation":
-            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(permute_col)
-        if sp_mc_selection == "shuffle":
-            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(shuffle_col)
-        if sp_mc_selection == "both":
-            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(permute_col)
-            return_tilesdf.iloc[1:,1:] = return_tilesdf.iloc[1:,1:].apply(shuffle_col)
-
-        pricesdf = sp_s0 * (1 + return_tilesdf).cumprod()
-
-        mcdf = pricesdf.copy()
-        mcdf.index = df.index
-        mcdf = mcdf.iloc[:,sp_mc_range[0]:sp_mc_range[1]]
-        # df.name = "YOU" if len(sp_ticker) == 0 else sp_ticker.upper()
-        # df.name = sp_ticker.upper()
-        # mcdf.name = f"MC{sp_mc_count} - {df.name}"
-        mcdf.name = df.name
-
-        if not mcdf.empty:
-            # st.markdown(f"### Chart {mcdf.name}")
-            sim_statsdf = mcdf.describe(include="all").T
-            sim_mean = sim_statsdf["mean"].mean()
-            sim_std = sim_statsdf["std"].mean()
-
-            st.markdown("---")
-            st.markdown(f"#### {mcdf.name}")
-            st.markdown(f"**{sp_mc_return_type.title()} Return Simulations**: {sp_mc_count}, **Mean**: {round(sim_mean, 5)}, **Stdev**: {round(sim_std, 5)}")
-
-            if st.checkbox(f"Show Chart", True, key="show_mcdf_chart"):
-                mc_fig = sample_chart(mcdf, fig_size=(stoch_plot_width, stoch_plot_height), colors=None).figure
-                st.pyplot(mc_fig)
-            _success(f"MC Ready")
-            # ready = st.success("MC Ready")
-            # tsleep(1.2)
-            # ready.empty()
-
-            # if not st.checkbox(f"MC Simulations Minimized", True, key="show_mcdf"): st.dataframe(mcdf)
-            if st.checkbox(f"Show DataFrame", True, key="show_mcdf_df"):
-                st.markdown("###")
-                st.dataframe(mcdf)
-                st.markdown("###")
-                # st.dataframe(mcdf.cumsum(axis=0))
-                # st.markdown("###")
-                # st.dataframe(mcdf.describe(include="all"))
-                # st.markdown("###")
-                # st.dataframe()
-
-
-def stochastic_selector(column=None, title="### Stochastic Processes"):
-    column = _is_st_column(column)
-    _help_txt = "help(ta.sample)"
-    column.markdown(title)
-    sandbox = column.checkbox("Options", False, "stochastic_sandbox")
-    if sandbox: stochastic_data()
-    expand_stochastic = column.checkbox(_help_txt, False, "expand_stochastic")
-    if expand_stochastic: st.help(ta.sample)
-
-
-def ta_sample_noises():
-    return ta.sample().noises
-
-def ta_sample_processes():
-    return ta.sample().processes
-
-
-def yf_chart(df:pd.DataFrame, fig_size=(16, 10), stats:bool = False, linreg:bool = None):
-    if df.empty: return
-    linreg = bool(linreg) if linreg is not None and isinstance(linreg, bool) else False
-
-    title_ = f"{df.name}" if hasattr(df, "name") else "Local"
-    colors = ["black", "gray", "green", "lightgreen", "pink", "red", "orange", "orange"]
-
-    volumedf = pd.DataFrame()
-    if "volume" in df.columns:
-        volumedf = pd.DataFrame(df["volume"])
-        del df["volume"]
-
-    if stats:
-        min_, max_ = np.min(df.values), np.max(df.values)
-        mu_, std_ = np.mean(df.values), np.std(df.values)
-        df["mu"] = mu_
-        df["mu-2std"] = mu_ - 2 * std_
-        df["mu-std"] = mu_ - std_
-        df["mu+std"] = mu_ + std_
-        df["mu+2std"] = mu_ + 2 * std_
-        df["min"] = min_
-        df["max"] = max_
-
-        if linreg:
-            df.ta.linreg(close=df.iloc[:,0], length=df.shape[0], fill_method="bfill", append=True)
-            colors.append("magenta")
-
-    if "open" in df.columns: del df["open"]
-    if "high" in df.columns: del df["high"]
-    if "low" in df.columns: del df["low"]
-
-    price_fig = df.plot(figsize=fig_size, title=title_, color=colors, grid=True)
-
-    if volumedf.empty:
-        volume_fig = None
-    else:
-        volume_fig = volumedf.plot(figsize=(fig_size[0], 2.5), kind="area", stacked=False, title=f"{title_} Volume", color=["gray"], alpha=0.6, grid=True)
-    return price_fig, volume_fig
-
-
-def yfinance_data(title="### Yahoo Finance Sandbox"):
-    st.markdown(title)
-    base = st.container()
-    with base:
-        stoch_parameters = st.container()
-        with stoch_parameters:
-            st.markdown("#### Parameters")
-            with st.form("yfinance_form"):
-
-                yf_p11, yf_p12, yf_p13 = st.columns(3)
-                yf_ticker = yf_p11.text_input("Ticker", "SPY", key="yf_ticker")
-
-                yf_period = yf_p12.selectbox("Period", "1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max".split(","))
-                yf_interval = yf_p13.selectbox("Interval", "1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo".split(","))
-
-                yf_p21, yf_p22, yf_p23 = st.columns(3)
-                yf_dividends = yf_p21.checkbox("Dividends", False)
-                yf_stocksplits = yf_p22.checkbox("Stock Splits", False)
-                yf_stats = yf_p23.checkbox("Stats", False, key="yf_stats")
-
-                yf_params = {
-                    "ticker": "SPY" if len(yf_ticker) == 0 else yf_ticker.upper(),
-                    "period": yf_period,
-                    "interval": yf_interval,
-                    "lc_cols": True
-                }
-
-                yf_submitted = st.form_submit_button("Submit")
-
-            yfdf = ta.df
-            if yf_submitted:
-                yfdf = ta.df.ta.ticker(**yf_params)
-
-                _drop_cols = []
-                if not yf_dividends: _drop_cols.append("dividends")
-                if not yf_stocksplits: _drop_cols.append("stock splits")
-                yfdf.drop(columns=_drop_cols, errors="ignore", inplace=True)
-
-                if "yfdf" not in st.session_state["pta"]:
-                    st.session_state["pta"]["yfdf"] = yfdf
-
-                if not yfdf.empty:
-                    st.markdown("### Chart")
-                    yfdf_full = yfdf.copy()
-
-                    _success(f"{yf_ticker.upper()} Ready")
-                    # ready = st.success(f"{yf_ticker.upper()} YF Data Ready")
-                    # tsleep(1.25)
-                    # ready.empty()
-                    # pdf, vdf = yf_chart(yfdf, fig_size=(16, 8), stats=yf_stats, linreg=True)
-                    # st.pyplot(pdf.figure)
-                    # st.pyplot(vdf.figure)
-
-def yfinance_selector(column=None, title="### Yahoo Finance"):
-    column = _is_st_column(column)
-    _help_txt = "help(ta.yf)"
-    column.markdown(title)
-    sandbox = column.checkbox("Options", False, "yfinance_options")
-    if sandbox: yfinance_data()
-    expand_yfinance = column.checkbox(_help_txt, False, "expand_yfinance")
-    if expand_yfinance: st.help(ta.yf)
-
-
-### Streamlit Sections
-
-
-def description():
-    expander = st.expander("", True)
-    with expander:
-        bcol1, logocol, bcol2 = st.columns(3)
-        with bcol1: st.write("")
-        with logocol: st.image("https://raw.githubusercontent.com/twopirllc/pandas-ta/main/images/logo.png")
-        with bcol2: st.write("")
-
-        st.markdown("""
-        # Pandas TA
-        ## A Technical Analysis Library in Python 3
-        ####""")
-
-        st.markdown("""
-        [![license](https://img.shields.io/github/license/twopirllc/pandas-ta)](#license)
-        [![Python Version](https://img.shields.io/pypi/pyversions/pandas-ta?style=flat)](https://pypi.org/project/pandas_ta/)
-        [![PyPi Version](https://img.shields.io/pypi/v/pandas-ta?style=flat)](https://pypi.org/project/pandas_ta/)
-        [![Package Status](https://img.shields.io/pypi/status/pandas-ta?style=flat)](https://pypi.org/project/pandas_ta/)
-        [![Downloads](https://img.shields.io/pypi/dm/pandas_ta?style=flat)](https://pypistats.org/packages/pandas_ta)
-        [![Stars](https://img.shields.io/github/stars/twopirllc/pandas-ta?style=flat)](#stars)
-        [![Forks](https://img.shields.io/github/forks/twopirllc/pandas-ta?style=flat)](#forks)
-        [![Used By](https://img.shields.io/badge/used_by-184-orange.svg?style=flat)](#usedby)
-        [![Contributors](https://img.shields.io/github/contributors/twopirllc/pandas-ta?style=flat)](#contributors)
-        [![Issues](https://img.shields.io/github/issues-raw/twopirllc/pandas-ta?style=flat)](#issues)
-        [![Closed Issues](https://img.shields.io/github/issues-closed-raw/twopirllc/pandas-ta?style=flat)](#closed-issues)
-        [![Buy Me a Coffee](https://img.shields.io/badge/buy_me_a_coffee-orange.svg?style=flat)](https://www.buymeacoffee.com/twopirllc)
-
-        ###
-        _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that leverages the Pandas package with more than 140 Indicators and Utility functions and more than 60 TA Lib Candlestick Patterns. Many commonly used indicators are included, such as: _Candle Pattern_(**cdl_pattern**), _Simple Moving Average_ (**sma**) _Moving Average Convergence Divergence_ (**macd**), _Hull Exponential Moving Average_ (**hma**), _Bollinger Bands_ (**bbands**), _On-Balance Volume_ (**obv**), _Aroon & Aroon Oscillator_ (**aroon**), _Squeeze_ (**squeeze**) and **_many more_**.
-
-        ###
-        ### Example Streamlit Application
-        **This** is a Basic [Streamlit](https://streamlit.io/) Application using the following packages: ```matplotlib```, ```numpy```, ```pandas```, ```pandas_ta```, ```stochastic```, ```streamlit```, ```yfinance```. It is currently not optimized but Pull Requests are open for those that want to help make it better. :sunglasses: It's purpose is to help the user easily find help on indicators and functions as well as allow them to download or generate data for analysis. While this has been initially built with ```matplotlib``` for charting, feel free to modify and include your charts that [Streamlit Charts](https://docs.streamlit.io/library/api-reference/charts) supports.
-
-        To get the most of this App, install:
-        """)
-        st.code("(env) $ pip install matplotlib numpy pandas pandas_ta stochastic streamlit yfinance", "shell")
-
-        st.markdown("""Run the App and check your browser tab for the application.""")
-        st.code("(env) $ streamlit run pta_streamlit.py", "shell")
-        st.markdown("""
-        ####
-        ### For more information
-        See the **Pandas TA** [README](https://github.com/twopirllc/pandas-ta) for more details on basic usage and the [Example Notebooks](https://github.com/twopirllc/pandas-ta/tree/main/examples) for detailed usage.
-        """)
-
-
-def installation(title="**Installation**"):
-    expander = st.expander("", True)
-    with expander:
-        st.markdown(f"""
-        {title}
-        ===================
-
-        Stable
-        ------
-        The ```pip``` version is the last stable release. Version: *0.3.14b*""")
-        st.code("(env) $ pip install pandas_ta", "shell")
-
-        st.markdown("""
-        Latest Version
-        --------------
-        Best choice! Version: *0.3.32b*
-        """)
-        st.code("(env) $ pip install -U git+https://github.com/twopirllc/pandas-ta", "shell")
-
-        st.markdown("""
-        Cutting Edge
-        ------------
-        This is the _Development Version_ which could have bugs and other undesireable side effects. Use at own risk!
-        """)
-        st.code("(env) $ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development", "shell")
-
-
-def tahelp(title="Help"):
-    expander = st.expander("", True)
-    with expander:
-        st.markdown(f"""
-        ## {title}
-        """)
-        st.code("""
-import pandas as pd
-import pandas_ta as ta
-
-# Create a DataFrame so 'ta' can be used.
-df = pd.DataFrame()
-
-# Help about this, 'ta', extension
-help(df.ta)
-
-# List of all indicators
-df.ta.indicators()
-
-# Help about an indicator such as bbands
-help(ta.bbands)
-        """)
-
-        st.markdown("####")
-        st.markdown("""### Categories""")
-        all_tai = [""]
-        all_tai += e.ta.indicators(as_list=True, exclude=exclude_indicators())
-        ind_help = st.selectbox(f"Indicators [{len(all_tai) - 1}]", all_tai)
-        expand_indicators = st.checkbox("Expand", True, "expand_indicators")
-        if expand_indicators and hasattr(ta, ind_help):
-            st.help(getattr(ta, ind_help))
-
-        st.markdown("####")
-        other = [
-            "", "category_files", "combination", "df_dates", "df_month_to_date",
-            "df_quarter_to_date", "df_year_to_date", "erf", "geometric_mean",
-            "get_drift", "get_offset", "hpoly", "inv_norm",
-            "is_datetime_ordered", "linear_regression", "log_geometric_mean",
-            "non_zero_range", "pascals_triangle", "sample", "signed_series",
-            "symmetric_triangle", "tal_ma", "to_utc", "unsigned_differences",
-            "verify_series", "yf", "zero"
-        ]
-        other_help = st.selectbox(f"Other [{len(other) - 1}]", other)
-        expand_other = st.checkbox("Expand", True, "expand_other")
-        if expand_other and hasattr(ta, other_help):
-            st.help(getattr(ta, other_help))
-
-
-def data(title="## Data"):
-
-    expander = st.expander("", True)
-    with expander:
-        st.markdown(title)
-        st.markdown("""**Pandas TA** includes wrappers around ```stochastic```, to generate sample realizations,
-        or ```yfinance```, download US Market data from Yahoo Finance. If you have local _csv_ or _json_ files, you can import them from the left sidebar under "Local".""")
-
-        # st.write(locals())
-        # st.write(st.session_state)
-        if df is not None:# and not df.empty:
-            st.markdown("### Local File")
-            st.markdown("###")
-            st.markdown(f"**Shape**: {df.shape}")
-            # st.markdown(f"**Index**: {', '.join(df.index)}")
-            st.markdown(f"**Index**: {df.index}, **Datetime Ordered**: {ta.is_datetime_ordered(df)}")
-            st.markdown(f"**Columns**: {', '.join(df.columns)}")
-            if st.checkbox("Set Index", True, key="df_set_index"):
-                index_col = st.selectbox("Index by:", [""] + list(df.columns))
-                st.markdown(f"### index_col: {index_col}")
-                if index_col:
-                    df.set_index(pd.DatetimeIndex(df[index_col]), inplace=True, drop=True)
-                    df.drop(index_col, axis=1, inplace=True)
-
-            st.markdown(f"### {df.name} Details")
-            df_detail_11, df_detail_12 = st.columns(2)
-            df_detail_11.markdown(f"**Start**: {df.index[0]}")
-            df_detail_12.markdown(f"**End**: {df.index[-1]}")
-
-                    # df.set_index(pd.DatetimeIndex(index_col), drop=True)
-            st.dataframe(df)
-        # col11, col12 = st.columns(2)
-        # if ta.Imports["stochastic"] and ta.Imports["yfinance"]:
-        #     stochastic_selector(col11)
-        #     yfinance_selector(col12)
-        # elif ta.Imports["stochastic"]:
-        #     stochastic_selector(col11)
-        #     with col12.container():
-        #         st.markdown("#### For Yahoo Finance Data")
-        #         st.code("$ pip install yfinance", "shell")
-        # elif ta.Imports["yfinance"]:
-        #     yfinance_selector(col11)
-        #     with col12.container():
-        #         st.markdown("#### For Stochastic Data")
-        #         st.code("$ pip install stochastic", "shell")
-        # else: pass
-
-
-def analysis(title="Analysis"):
-    expander = st.expander("", True)
-    with expander:
-        st.markdown(f"""
-        ## {title}
-        Pandas TA has three [Programming Conventions](https://github.com/twopirllc/pandas-ta#programming-conventions) to support your programming "style". Below are examples of each convention with indicators shown on the README.
-        """)
-        st.code("""
-# Standard imports
-import pandas as pd
-import pandas_ta as ta
-        """)
-
-        st.markdown("####")
-        st.markdown("""### Data""")
-        st.markdown("Select one of the data source options from the Data section.")
-
-        df_ = pd.DataFrame()
-        if "sampledf" in st.session_state["pta"]:
-            _success("Using Stochastic Data.")
-            df_ = st.session_state["pta"]["sampledf"]
-        if "localdf" in st.session_state["pta"] and st.session_state["pta"]["local_file_name"] is not None:
-            local_file_name = st.session_state["pta"]["local_file_name"]
-            _success(f"Using local file: {local_file_name}")
-            df_ = st.session_state["pta"]["localdf"]
-        if "yfdf" in st.session_state["pta"]:
-            _success("Using Yahoo Finance Download.")
-            df_ = st.session_state["pta"]["yfdf"]
-        if df_.empty:
-            st.info("No ohlcv data selected from 'Data' Section")
-
-        if not df_.empty:
-            st.markdown("#")
-
-            expand_standard_style = st.checkbox("Show Standard Style", False, "expand_standard_style")
-            if expand_standard_style:
-                st.markdown("##")
-                st.markdown("""## Standard (TA Lib style)""")
-                with st.echo():
-                    df = df_.copy()
-                    sma10 = ta.sma(df["close"], length=10)
-                    donchiandf = ta.donchian(df["high"], df["low"], lower_length=10, upper_length=15)
-                    ema10_ohlc4 = ta.ema(ta.ohlc4(df["open"], df["high"], df["low"], df["close"]), length=10)
-                    # You handle the input/output
-                    sma10
-                    donchiandf
-                    ema10_ohlc4
-
-            expand_df_style = st.checkbox("Show DataFrame Extension Style", False, "expand_df_style")
-            if expand_df_style:
-                st.markdown("##")
-                st.markdown("""### DataFrame Extension""")
-                with st.echo():
-                    df = df_.copy()
-                    sma10 = df.ta.sma(length=10, append=True)
-                    df.ta.ema(close=df.ta.ohlc4(append=True), length=10, suffix="OHLC4", append=True)
-                    donchiandf = df.ta.donchian(lower_length=10, upper_length=15, append=True)
-                    # Append with 'append=True' when using the DataFrame Extension .ta.
-                    df
-                    # As well as Standard Style returns
-                    sma10
-                    donchiandf
-
-            expand_pta_style = st.checkbox("Show Pandas TA Style", False, "expand_pta_style")
-            if expand_pta_style:
-                st.markdown("##")
-                st.markdown("""### Pandas TA Strategy""")
-                with st.echo():
-                    df = df_.copy()
-                    # (1) Create the Strategy
-                    MyStrategy = ta.Strategy(
-                        name="DCSMA10",
-                        ta=[
-                            {"kind": "ohlc4"},
-                            {"kind": "sma", "length": 10},
-                            {"kind": "donchian", "lower_length": 10, "upper_length": 15},
-                            {"kind": "ema", "close": "OHLC4", "length": 10, "suffix": "OHLC4"},
-                        ]
-                    )
-
-                    # (2) Run the Strategy
-                    df.ta.strategy(MyStrategy)
-                    df
-
-
-def strategy_checker(title="## Strategy Checker"):
-    # expander = st.expander("", True)
-    # with expander:
-    with st.expander("", True):
-        st.markdown(title)
-        st.markdown("""
-        Check your **Pandas TA** Strategy code.
-
-        When testing JSON Strategy code, use array notation and _not_ tuple notation for 'col_names' and 'col_numbers' keywords. The arrays _will_ be converted to tuples in the process.
-        """)
-
-        # strategy = None
-        with st.form(key="json_strategy_checker"):
-            if "strategy_txt" not in st.session_state["pta"]:
-                st.session_state["pta"]["strategy_txt"] = strategy_sample
-
-            json_strategy_text = st.text_area("Paste JSON below.", height=300, value=st.session_state["pta"]["strategy_txt"])
-            submit_json_strategy_check = st.form_submit_button("Check")
-            if submit_json_strategy_check:
-                strategy = check_strategy(json_strategy_text, False)
-                if strategy is not None and not isinstance(strategy, bool):
-                    _success(f"{strategy.name} is valid.")
-
-                    strat_check_col1, strat_check_col2, strat_check_col3, strat_check_col4 = st.columns(4)
-                    strat_check_col1.markdown("**Name**")
-                    strat_check_col2.write(strategy.name)
-                    if hasattr(strategy, "description") and len(strategy.description):
-                        strat_check_col3.markdown("**Description**")
-                        strat_check_col4.write(strategy.description)
-                    st.markdown(f"**TA[{len(strategy.ta)}]**")
-                    st.json(strategy.ta)
-
-                    # if "strategy_txt" not in st.session_state["pta"]:
-                    st.session_state["pta"]["strategy_txt"] = json_strategy_text
-
-
-                else:
-                    st.warning("Not a valid Pandas TA JSON Strategy")
-
-
-def chart(title="Charting"):
-    st.markdown(f"""
-    ## {title}
-    """)
-
-
-def disclaimer(title="## Disclaimer"):
-    st.markdown(f"""
-    {title}
-
-    ###### All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, or individual’s trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
-
-    ###### Any opinions, news, research, analyses, prices, or other information offered is provided as general market commentary, and does not constitute investment advice. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from use of or reliance on such information.
-    """)
-
-
-def sidebar(title="Site Options"):
-    st.sidebar.markdown(f"# {title}")
-
-    st.sidebar.container()
-    container = st.sidebar.container()
-    # with container:
-
-        # st.sidebar.checkbox("Show Installation", True)
-        # st.sidebar.checkbox("Show Disclaimer", True)
-        # show_installation = st.sidebar.checkbox("Show Installation", True)
-        # show_disclaimer = st.sidebar.checkbox("Show Disclaimer", True)
-    return st
-
-def sidebar_data_options(title="## Data"):
-    with st.sidebar.expander("Options", True):
-        sources = ["Local"]
-        if ta.Imports["stochastic"]: sources.append("Stochastic")
-        if ta.Imports["yfinance"]: sources.append("Yahoo Finance")
-
-        data_choice = st.radio("Select", sources, key="sb_data_choice")
-
-        if data_choice == "Stochastic":
-            st.checkbox("Options", False, key="sb_stochastic_options")
-            st.checkbox("Help", False, key="sb_stochastic_help")
-
-        if data_choice == "Yahoo Finance":
-            st.checkbox("Options", False, key="sb_yfinance_options")
-            st.checkbox("Help", False, key="sb_yfinance_help")
-
-        if data_choice == "Local":
-            file = st.file_uploader("Choose a file", type=["csv", "json"], key="sb_uploaded_file")
-            if file is not None:
-                # file_suffix = file.name.split(".")[1]
-                _success(f"{file.name} Ready")
-
-                # if "local_file_name" not in st.session_state["pta"]:
-                #     st.session_state["pta"]["local_file_name"] = file.name
-
-                if st.session_state["sb_uploaded_file"] is not None:
-                    df = _load_file(st.session_state["sb_uploaded_file"])
-                    df.name = file.name.split(".")[0]
-
-                # if "localdf" not in st.session_state["pta"]:
-                #     st.session_state["pta"]["localdf"] = localdf
-
-                # if st.session_state["sb_uploaded_file"] is None:
-                #     if "data_local_filename" not in st.session_state:
-                #         st.session_state["data_local_filename"] = file.name
-
-                # st.write(st.session_state.get("data_local_filename"))
-                # st.write(st.session_state.get("sb_uploaded_file"))
-                # if st.checkbox(f"Loaded {file.name} | {localdf.shape}", False, key="show_localdf"):
-                #     st.write("1, 2, 3, ...")
-                return df
-
-            # if file_ is not None:
-            #     file_suffix = file_.name.split(".")[1]
-            #     # fd = file_.read()
-            #     # st.write([x for x in dir(file_) if not x.startswith('_')])
-            #     _success(f"{file_.name} Ready")
-
-            #     if "local_file_name" not in st.session_state["pta"]:
-            #         st.session_state["pta"]["local_file_name"] = file_.name
-
-            #     # st.write(st.session_state)
-
-            #     if file_suffix == "csv":
-            #         localdf = pd.read_csv(file_)
-            #     elif file_suffix == "json":
-            #         localdf = pd.read_json(file_)
-            #     else: pass
-
-            #     if "localdf" not in st.session_state["pta"]:
-            #         st.session_state["pta"]["localdf"] = localdf
-
-            # if not localdf.empty and file is not None:
-            #     st.markdown("### Chart")
-            #     localdf.name = file.name
-            #     # localdf.name = Path(file).name.split(".")[0]
-            #     localdf_full = localdf.copy()
-            #     # pdf, vdf = yf_chart(localdf, fig_size=(16, 8), stats=False, linreg=False)
-            #     # st.pyplot(pdf.figure)
-            #     # if vdf is not None: st.pyplot(vdf.figure)
-            #     _success(f"{localdf.name} Ready")
-
-
-if __name__ == "__main__":
-    st.set_page_config(
-        "Pandas TA Help and Examples",
-        page_icon=":bar-chart:",
-        initial_sidebar_state="expanded",
-        layout="wide"
-    )
-    # st.sidebar.markdown(f"**Date**: {dt_today.strftime('%Y-%m-%d')}")
-    show = {}
-
-    # Sidebar
-    page_options = st.sidebar.container()
-    with page_options:
-        st.sidebar.markdown("# Sections")
-        show["description"] = st.sidebar.checkbox("Description", False, key="sb_description_check")
-        show["installation"] = st.sidebar.checkbox("Installation", False, key="sb_installation_check")
-        show["help"] = st.sidebar.checkbox("Help", False, key="sb_help_check")
-
-        show["data"] = st.sidebar.checkbox("Data", True, key="sb_data_check")
-        df = sidebar_data_options() if show["data"] else None
-
-        show["analysis"] = st.sidebar.checkbox("Analysis", False, key="sb_analysis_check")
-        show["strategy_checker"] = st.sidebar.checkbox("Strategy Checker", False, key="sb_strategy_check")
-        show["chart"] = st.sidebar.checkbox("Charting", False, key="sb_chart_check")
-        show["disclaimer"] = st.sidebar.checkbox("Disclaimer", False, key="sb_disclaimer_check")
-
-    st.sidebar.markdown("---")
-    st.sidebar.markdown("# Settings")
-    show["display"] = st.sidebar.checkbox("Display", True, key="sb_display_check")
-    # width_slider = st.sidebar.slider("Width Percent", value=95, min_value=85, max_value=100)
-    # _max_width_(pct_width=width_slider)
-    if show["display"]:
-        sb_plot_expander = st.sidebar.expander("Stochastic Plot Size", False)
-        with sb_plot_expander:
-            stoch_plot_width = st.number_input("Width", value=float(16), min_value=float(1), key="sb_stoch_plot_width")
-            stoch_plot_height = st.number_input("Height", value=float(8), min_value=float(1), key="sb_stoch_plot_height")
-
-    # Main Page
-    if show["description"]: description()
-    if show["installation"]: installation()
-    if show["help"]: tahelp()
-    if show["data"]:
-        data()
-        # Options for Stoch and YF here
-        def _sb_stochastic_options():
-            if "sb_stochastic_options" in st.session_state and st.session_state["sb_stochastic_options"]:
-                stochastic_data()
-        def _sb_stochastic_help():
-            if "sb_stochastic_help" in st.session_state and st.session_state["sb_stochastic_help"]:
-                st.markdown("---")
-                st.markdown("#### Stochastic Help")
-                st.help(ta.sample)
-
-        def _sb_yfinance_options():
-            if "sb_yfinance_options" in st.session_state and st.session_state["sb_yfinance_options"]:
-                st.markdown("---")
-                st.markdown("### Yahoo Finance Options")
-                yfinance_data()
-
-                if not st.session_state["yfdf"].empty:
-                    st.dataframe(st.session_state["yfdf"])
-                    # st.dataframe(yfdf_full)
-                    # if st.checkbox(f"Show DataFrame: {yf_ticker.upper()} | {yfdf.shape}", False, key="show_yfdf"):
-                        # st.dataframe(yfdf_full)
-
-        def _sb_yfinance_help():
-            if "sb_yfinance_help" in st.session_state and st.session_state["sb_yfinance_help"]:
-                st.markdown("---")
-                st.markdown("#### Yahoo Finance Help")
-                st.help(ta.yf)
-
-        _sb_stochastic_options()
-        _sb_stochastic_help()
-
-        _sb_yfinance_options()
-        _sb_yfinance_help()
-    if show["analysis"]: analysis()
-    if show["strategy_checker"]: strategy_checker()
-    if show["chart"]: chart()
-    if show["disclaimer"]: disclaimer()
-    # imports()
-
-st.markdown("###")
-st.markdown("---")
-st.write("st.session_state")
-st.write(st.session_state)
diff --git a/jnb/sample.json b/jnb/sample.json
deleted file mode 100644
index 1399672..0000000
--- a/jnb/sample.json
+++ /dev/null
@@ -1,20 +0,0 @@
-{
-    "name": "Sample JSON Strategy",
-    "description": "Sample JSON Strategy Test",
-    "ta": [
-        {"kind": "ema", "length": 10, "sma": false},
-        {"kind": "sma", "length": 50, "talib": false},
-        {
-            "kind": "bbands",
-            "talib": true,
-            "col_numbers": [0, 1, 2],
-            "col_names": ["BBL", "BBM", "BBU"]
-        },
-        {
-            "kind": "atr",
-            "length": 50,
-            "talib": true,
-            "col_names": ["ATR"]
-        }
-    ]
-}
\ No newline at end of file
diff --git a/jnb/simple_strategy.json b/jnb/simple_strategy.json
deleted file mode 100644
index cb308fc..0000000
--- a/jnb/simple_strategy.json
+++ /dev/null
@@ -1,12 +0,0 @@
-{
-    "name": "Simple Strategy",
-    "description": "Sample JSON Strategy Test 2",
-    "ta": [
-        {"kind": "ema", "length": 8, "sma": true},
-        {"kind": "ema", "length": 21, "talib": true},
-        {"kind": "sma", "length": 50, "talib": true},
-        {"kind": "sma", "length": 200, "talib": true},
-        {"kind": "rsi", "talib": true},
-        {"kind": "obv"}
-    ]
-}
\ No newline at end of file
diff --git a/jnb/talit.py b/jnb/talit.py
deleted file mode 100644
index 6530119..0000000
--- a/jnb/talit.py
+++ /dev/null
@@ -1,499 +0,0 @@
-# -*- coding: utf-8 -*-
-import datetime
-# import os
-from json import load as json_load
-from pandas_ta.candles.ha import ha
-from pathlib import Path
-
-import numpy as np
-import pandas as pd
-import matplotlib.pyplot as plt
-import matplotlib.colors as colors
-from matplotlib import rc
-import mplfinance as mpf
-from tqdm.notebook import trange, tqdm
-import streamlit as st
-
-import pandas_ta as ta
-
-
-dt_today = datetime.datetime.now()
-e = pd.DataFrame()
-
-
-def _max_width_(st, pct_width:int = None):
-    """"https://discuss.streamlit.io/t/custom-render-widths/81/8"""
-    if pct_width is None:
-        max_width_str = f"max-width: 2000px;"
-    else:
-        max_width_str = f"max-width: {pct_width};"
-
-    st.markdown(
-        f"""
-    <style>
-    .reportview-container .main .block-container{{
-        {max_width_str}
-    }}
-    </
-    """, unsafe_allow_html=True)
-
-
-@st.cache
-def app_source():
-    result = ["stochastic"]
-    if ta.Imports["yfinance"]: result.append("yfinance")
-    if ta.Imports["alphaVantage-api"]: result.append("AlphaVantage")
-    return result
-
-
-def asset_class():
-    return ["DEMO", "Equity", "Crypto", "FX"]
-
-
-def imports(title="Available Functionality", columns:int = 4):
-    st.sidebar.markdown(f"""## {title}""")
-    total_items = len(ta.Imports.keys())
-    for i in range(0, len(ta.Imports.keys()) - 1):
-        j = 0
-        for k,v in ta.Imports.items():
-            col = st.columns(total_items // columns)
-            # col[].write(k, v)
-            # col[j].checkbox(k, v)
-            print(i, j, (i + j) % columns, (i + j) % columns < columns, k, v)
-            if (i + j) % columns == columns: break
-            j += 1
-            # st.write((i + j) % columns, k, v)
-            # col.write(k)
-            # j += 1
-            # if j + 1 == total_items: break
-            # col[i % cols].write(k)
-            # st.write(k, v)
-            # st.write(k, v)
-            # col[(i + j) % cols].checkbox(k, v, f"i{i}{j}{cols}")
-            # col[1].checkbox(obj[0], obj[1], f"ic1_{i}")
-            # col[2].checkbox(obj[0], obj[1], f"ic2_{i}")
-            # st.write(i, type(obj))
-            # st.write(i % cols, obj[0], obj[1])
-    print("\n\n")
-
-
-def load_strategy(path:str):
-    strat, json_strat = None, json_load(open(path))
-
-    strat = json_strat[0] if isinstance(json_strat, list) and len(json_strat) == 1 else json_strat
-
-    for indicator in strat["ta"]:
-        if "col_numbers" in indicator and len(indicator["col_numbers"]):
-            indicator["col_numbers"] = tuple(indicator["col_numbers"])
-        if "col_names" in indicator and len(indicator["col_names"]):
-            indicator["col_names"] = tuple(indicator["col_names"])
-
-    if strat is not None and hasattr(strat, "description") and len(strat):
-        return ta.Strategy(name=strat["name"], ta=strat["ta"], description=strat["description"])
-    else:
-        return ta.Strategy(name=strat["name"], ta=strat["ta"])
-
-
-def return_file(path, ftype:str="json"):
-    result, p = [], Path(path)
-    for item in p.iterdir():
-        if item.is_file() and item.suffix == f".{ftype}":
-            result.append(item)
-    if len(result) == 0: result.append("")
-    return result
-
-
-def ta_sample_noises():
-    return ta.sample().noises
-
-def ta_sample_processes():
-    return ta.sample().processes
-
-
-def ticker_df(data_source:list, ticker:str):
-    df = None
-    print(f"[!] source: {data_source}")
-    if isinstance(data_source, str) and len(data_source) > 0:
-        _dsl = data_source.lower()
-        if _dsl == "yfinance":
-            # print(f"[!] source: yf")
-            df = ta.df.ta.ticker(ticker, period="max", interval="1d", lc_cols=True)
-            df.drop(columns=["dividends", "stock splits"], inplace=True)
-        else: #"stochastic"
-            print(f"[!] stochastic")
-            # st.sidebar.selectbox("Source", ["stochastic"])
-            # df = ta.sample(s0=np.sample(np.array([1, 2, 5, 10, 13, 25, 42, 50, 68])), process="gbm", length=2 * ta.RATE["TRADING_DAYS_YEAR"], verbose=True).df
-            # print(np.sample(np.array([1, 2, 5, 10, 13, 25, 42, 50, 68])))
-            df = ta.sample(s0=13, process="gbm", noise=None, length=ta.RATE["TRADING_DAYS_PER_YEAR"]).df
-    return df
-
-
-def ticker_list():
-    return ["SPY", "BTC-USD", "ETH-USD", "SPY", "AA", "AAPL", "BAC", "PLTR", "SQ", "TLT", "TWTR", "XLF", "XLK", "OCUL"]
-
-
-def main(*args, **kwargs):
-    # study_path = kwargs.pop("study_path", "sample.json")
-
-    st.markdown("# Pandas TAlit")
-    st.markdown("---")
-
-    # Sidebar
-    st.sidebar.markdown("# OPTIONS")
-    st.sidebar.markdown(f"**Date**: {dt_today.strftime('%Y-%m-%d')}")
-
-    # # selection_col1.markdown("### Selection")
-    # options_expander = st.expander("Options")
-    # oe_col1, oe_col2, oe_col3, oe_col4 = options_expander.columns(4)
-    # # my_expander.write('Hello there!')
-    # # clicked = my_expander.button('Click me!')
-    # # assets_ = my_expander.selectbox("Asset", asset_class())
-
-    # assets_ = oe_col1.selectbox("Asset", asset_class())
-    # source_ = oe_col2.selectbox("Source", app_source())
-    # ticker_ = oe_col3.selectbox("Ticker", ticker_list())
-    # bars_ = oe_col4.selectbox("Last", ["30", "60", "90", "180"])
-
-    # study_expander = st.expander("Studies")
-    # study_col1, study_col2, study_col3 = study_expander.columns(3)
-    # study_path = study_col1.text_input("Directory", ".")
-    # study_submit = study_col3.button("Submit")
-
-    # # 
-    # study_path = return_file(study_path)
-
-    # # study_path = study_col2.selectbox("Study Path", "")
-    # study_ = study_col2.selectbox("File", study_path)
-    # # study = load_strategy(study_path)
-    # print(dir(study_))
-    # print(type(study_))
-    # study = None
-    # if isinstance(study_, Path) and study_.is_file():
-    #     study = load_strategy(study_)
-    #     print(study)
-    # # with selection_col1:
-    #     # assets_ = st.selectbox("Asset", asset_class())
-    #     # source_ = st.selectbox("Source", ["stochastic", "yfinance", "AlphaVantage"])
-    #     # ticker_ = st.selectbox("Ticker", ticker_list())
-    #     # bars_ = st.selectbox("Last", ["30", "60", "90", "180"])
-
-    # # srctkr_options_container = st.container()
-    # # with srctkr_options_container:
-    #     # st.markdown("# DATA")
-    #     # assets_ = st.selectbox("Asset", asset_class())
-    #     # source_ = st.selectbox("Source", ["stochastic", "yfinance", "AlphaVantage"])
-    #     # ticker_ = st.selectbox("Ticker", ticker_list())
-    #     # bars_ = st.selectbox("Last", ["30", "60", "90", "180"])
-
-
-    sb_container = st.sidebar.container()
-    with sb_container:
-        sb_expander = st.expander("Mode")
-        with sb_expander:
-            mode_ = st.radio("Select", ["DEMO", "DATA", "LIVE"])
-
-    with sb_container:
-        sb_expander = st.expander("Studies")
-        with sb_expander:
-            json_path = st.text_input("Directory", ".", key="json_dir")
-            json_path = return_file(json_path, "json")
-            json_file = st.selectbox(f"Files", json_path)
-            try:
-                study_ = load_strategy(json_file)
-                if len(study_.ta): st.text(f"Loaded: {study_.name}")
-            except FileNotFoundError:
-                st.text(f"[X] No JSON Strategy Found")
-
-    sampledf = e.copy()
-    with sb_container:
-        sb_expander = st.expander("Source")
-        with sb_expander:
-            if mode_ == "DATA":
-                source_ = st.radio("Select", [app_source()[0], "csv"])
-
-                # sampledf = e.copy()
-                if source_ == "stochastic":
-                    st.header(source_.title())
-                    source_container = st.container()
-                    with source_container:
-                        sp_name = st.text_input("Ticker")
-                        sp_s0 = st.number_input("Initial", value=42.0, min_value=float(0.01), step=float(0.01))
-                        sp_process = st.selectbox("Process", ta_sample_processes())
-                        sp_noise = st.selectbox("Noise", ta_sample_noises())
-                        sp_length = st.number_input("Length", value=1000, min_value=2, step=1)
-                        sp_random_number = st.number_input("Random # (0 is random)", value=0, min_value=0, step=1)
-                        sp_future = st.checkbox("Future", True)
-                        sp_positive = st.checkbox("Positive", True)
-                        sp_stats = st.checkbox("Stats", False)
-
-                        sample_params = {
-                            "name": None if len(sp_name) == 0 else sp_name,
-                            "s0": sp_s0,
-                            "process": sp_process,
-                            "noise": sp_noise,
-                            "length": sp_length,
-                            "future": sp_future,
-                            "positive": sp_positive,
-                            "random_number": sp_random_number if sp_random_number > 0 else None
-                        }
-                        sampledf = ta.sample(**sample_params).df
-
-            else:
-                source_ = st.radio("Select", app_source()[1:])
-
-    csvdf = e.copy()
-    if source_ == "csv":
-        with sb_container:
-            sb_expander = st.expander("Local")
-            with sb_expander:
-                csv_path = st.text_input("Directory", ".", key="csv_dir")
-                csv_path = return_file(csv_path, "csv")
-                csv_ = st.selectbox("CSV File", csv_path)
-                csvdf = pd.read_csv(csv_)
-                # st.write(csv_path)
-                # st.write(csv_)
-
-    if not csvdf.empty:
-        st.markdown("### Local Data")
-        if st.checkbox("Show Chart", True):
-            sample_colors = ["black", "blue"]
-            if "date" in csvdf.columns: csvdf.set_index("date", inplace=True)
-            if "datetime" in csvdf.columns: csvdf.set_index("datetime", inplace=True)
-            # sample_fig = csvdf.plot(figsize=(16, 10), title=csv_, color=sample_colors, grid=True).figure
-            sample_fig = csvdf["close"].plot(figsize=(16, 10), title=csv_, color=sample_colors, grid=True).figure
-            st.pyplot(sample_fig)
-        if st.checkbox("Show", False): st.dataframe(csvdf)
-
-    # sampledf = e.copy()
-    # if source_ == "stochastic":
-    #     with sb_container:
-    #         sb_expander = st.expander("Parameters")
-    #         with sb_expander:
-    #             sp_name = st.text_input("Ticker")
-    #             sp_s0 = st.number_input("Initial", value=42.0, min_value=float(0.01), step=float(0.01))
-    #             sp_process = st.selectbox("Process", ta_sample_processes())
-    #             sp_noise = st.selectbox("Noise", ta_sample_noises())
-    #             sp_length = st.number_input("Length", value=1000, min_value=2, step=1)
-    #             sp_random_number = st.number_input("Random # (0 is random)", value=0, min_value=0, step=1)
-    #             sp_future = st.checkbox("Future", True)
-    #             sp_positive = st.checkbox("Positive", True)
-    #             sp_stats = st.checkbox("Stats", False)
-
-    #             sample_params = {
-    #                 "name": None if len(sp_name) == 0 else sp_name,
-    #                 "s0": sp_s0,
-    #                 "process": sp_process,
-    #                 "noise": sp_noise,
-    #                 "length": sp_length,
-    #                 "future": sp_future,
-    #                 "positive": sp_positive,
-    #                 "random_number": sp_random_number if sp_random_number > 0 else None
-    #             }
-    #             sampledf = ta.sample(**sample_params).df
-
-    if not sampledf.empty:
-        st.markdown("## Stochastic Sample")
-        sample_colors = ["black", "blue"]
-        sample_fig = sampledf.plot(figsize=(16, 10), title=sampledf.name, color=sample_colors, grid=True).figure
-        st.pyplot(sample_fig)
-
-
-    yfdf = e.copy()
-    if source_ == "yfinance":
-        with sb_container:
-            sb_expander = st.expander("Parameters")
-            with sb_expander:
-                yf_ticker = st.text_input("Ticker", ticker_list()[0])
-                # yf_s0 = st.number_input("Initial", value=42.0, min_value=float(0.01), step=float(0.01))
-                # sp_process = st.selectbox("Process", ta_sample_processes())
-                # sp_noise = st.selectbox("Noise", ta_sample_noises())
-                # sp_length = st.number_input("Length", min_value=2, step=1)
-                yf_period = st.selectbox("Period", "1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max".split(","))
-                if yf_period in "1d,5d,1mo".split(","):
-                    yf_interval = st.selectbox("Interval", "1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo".split(","))
-                # sp_future = st.checkbox("Future", True)
-                # sp_positive = st.checkbox("Positive", True)
-                sp_stats = st.checkbox("Stats", False)
-
-                yf_params = {
-                    "ticker": None if len(yf_ticker) == 0 else yf_ticker,
-                #     "s0": sp_s0,
-                #     "process": sp_process,
-                #     "noise": sp_noise,
-                #     "length": sp_length,
-                #     "future": sp_future,
-                    "period": yf_period,
-                    "interval": yf_interval,
-                    "lc_cols": True
-                }
-                yfdf = ta.yf(**yf_params)
-
-    if not yfdf.empty:
-        st.markdown("## Yahoo Finance ")
-        yf_colors = ["black", "blue"]
-        yf_fig = yfdf.plot(figsize=(16, 10), title=yf_ticker, color=yf_colors, grid=True).figure
-        st.pyplot(yf_fig)
-
-    st.markdown("#")
-    ind_help = None
-    pta_help_expander = st.expander("Pandas TA Indicator Help")
-    with pta_help_expander:
-        all_tai = e.ta.indicators(as_list=True)
-        ind_help = st.selectbox(f"Indicators [{len(all_tai)}]", all_tai)
-        collapse_ta_help = st.checkbox("Collapse", False)
-        if not collapse_ta_help and hasattr(ta, ind_help):
-            st.help(getattr(ta, ind_help))
-
-
-    # with st.sidebar.form(key="Form1"):
-    #     st.header("Options")
-    #     ticker_ = st.text_input("Ticker", "")
-    #     _include_retweets = st.checkbox("Check")
-    #     _num_of_tweets = st.number_input("number input", 100)
-    #     form1_submitted = st.form_submit_button(label="Submit 🔎")
-
-    # date_options_container = st.sidebar.container()
-    # with date_options_container:
-    #     st.markdown("# DATE\n\n")
-        # start_date = st.sidebar.date_input("Start", datetime.date(2019, 1, 1))
-        # end_date = st.sidebar.date_input("End", dt_today)
-
-    # print(type())
-
-    # # dl_state = st.text(f"Loading '{source_}' data...")
-    # df = ticker_df(source_, ticker_)
-    # print(f"df[{type(df)}:{df.shape}]:\n{df}\n")
-    # # print(type(df))
-    # # dl_state = st.text("")
-
-    # if df is not None and study is not None and len(study.ta):
-    #     # dl_state = st.text(f"Running Strategy: {study.name}...")
-    #     df.ta.strategy(study, timed=True, verbose=True)
-    #     # dl_state = st.text("")
-    #     print(df)
-
-    # print(df.shape)
-    # print(ssdf.shape)
-
-    # print(df.iloc[:,:-20])
-    # print(df.tail(int(bars_)))
-    # df = ta.df.ta.ticker(tickerSymbol, period="max")
-    # tickerData = yf.Ticker(tickerSymbol) # Get ticker data
-    # tickerDf = tickerData.history(period='1d', start=start_date, end=end_date) #get the historical prices for this ticker
-    # st.write(tickerSymbol)
-
-    # if not df.empty and df.shape[0] > 0:
-    #     st.write(_data_header + f" - Last {bars_}")
-    #     st.dataframe(df[:-int(bars_)])
-    # else:
-        # st.write(_data_header)
-        # st.dataframe(df)
-
-    # if df is not None:# or not df.empty:
-    #     _df = df.copy()
-    #     _dre, _drs  = _df.index[-1].strftime('%Y-%m-%d'), _df.index[0].strftime('%Y-%m-%d')
-
-    #     _data_header = f"{ticker_}[{source_}| {df.shape}] from {_drs} to {_dre}"
-    #     if "volume" in _df.columns:
-    #         colors = ["black", "blue"]
-    #     else:
-    #         colors = ["black", "green", "red", "violet", "purple", "violet"]
-
-    #     if "volume" in df.columns:
-    #         print(_df.columns)
-    #         voldf = _df["volume"]
-    #         del _df["volume"]
-    #         if "OBV" in df.columns:
-    #             obvdf = _df["volume"]
-    #             del _df["volume"]
-
-    #         _df = _df.iloc[:,3:]
-    #         print(_df.columns)
-
-    #         # fig = _df.plot(figsize=(16, 10), title=_data_header, color=colors, grid=True).figure
-    #         # fig2 = voldf.plot(figsize=(16, 3), kind="bar", stacked=False, title=f"{_data_header} Volume", color=["silver"], grid=True).figure
-    #         fig2 = voldf.plot(figsize=(16, 3), title=f"{_data_header} Volume", color=["gray"], grid=True).figure
-    #         # st.pyplot(fig, clear_figure=False)
-    #         st.pyplot(fig2)
-    #     else:
-    #         fig = _df.plot(figsize=(16, 10), title=_data_header, color=colors, grid=True).figure
-    #         st.pyplot(fig)
-
-    #     # bars_ = 20
-    #     # if df.shape[0] > int(bars_):
-    #     #     st.write(_data_header + f" {bars_} bars")
-    #     #     st.dataframe(df[-int(bars_):], 1000)
-    #     # else:
-    #     #     st.write(_data_header)
-    #     #     st.dataframe(df, 1000)
-
-    # display_options_container = st.container()
-    # with display_options_container:
-    #     if st.checkbox(f"Maximize"):
-    #         _max_width_(st)
-
-    #     if st.checkbox(f"Last {bars_} bars"):
-    #         if df.shape[0] > int(bars_):
-    #             tmpdf = df.copy()
-    #             st.write(_data_header + f" ({bars_} bars)")
-    #             # tmpdf = tmpdf[-int(bars_):]
-    #             # st.dataframe(df[-int(bars_):], 1000)
-    #             st.dataframe(tmpdf, 1000)
-    #         else:
-    #             st.write(_data_header)
-    #             st.dataframe(df, 1000)
-
-
-    # def my_widget(key):
-    #     st.subheader("Hello there!")
-    #     return st.button(f"Click me {key}")
-
-    # # This works in the main area
-    # clicked = my_widget("first")
-
-    # # And within an expander
-    # my_expander = st.expander("Expand", expanded=True)
-    # with my_expander:
-    #     clicked = my_widget("second")
-
-    # if not st.sidebar.checkbox(f"Last {ta.RATE['TRADING_DAYS_PER_YEAR']} bars"):
-    #     st.table(df[:ta.RATE['TRADING_DAYS_PER_YEAR']])
-    # elif st.sidebar.checkbox(f"Last {last_bars} bars"):
-    #     st.table(df[:int(last_bars)])
-
-    # Ticker information
-    # string_logo = '<img src=%s>' % tickerData.info['logo_url']
-    # st.markdown(string_logo, unsafe_allow_html=True)
-
-    # string_name = tickerData.info['longName']
-    # st.header('**%s**' % string_name)
-
-    # string_summary = tickerData.info['longBusinessSummary']
-    # st.info(string_summary)
-
-    # Ticker data
-    # st.header('**Ticker data**')
-    # st.write(tickerDf)
-
-    # Bollinger bands
-    # st.header('**Bollinger Bands**')
-    # qf = cf.QuantFig(tickerDf,title='First Quant Figure',legend='top',name='GS')
-    # qf.add_bollinger_bands()
-    # fig = qf.iplot(asFigure=True)
-    # st.plotly_chart(fig)
-
-    ####
-    #st.write('---')
-    #st.write(tickerData.info)
-
-    # show = {}
-    # if show["data"]:
-    #     sb_data_expander = st.sidebar.expander("Source", True)
-    #     with sb_data_expander:
-    #         # stoch_plot_width = st.number_input("Width", value=float(16), min_value=float(1), key="sb_stoch_plot_width")
-    #         # stoch_plot_height = st.number_input("Height", value=float(8), min_value=float(1), key="sb_stoch_plot_height")
-    #         data_source_ = st.radio("Select", ["local", "stochastic", "yfinance"], key="")
-
-
-if __name__ == "__main__":
-    main()
\ No newline at end of file
diff --git a/jnb/test.json b/jnb/test.json
deleted file mode 100644
index 452e349..0000000
--- a/jnb/test.json
+++ /dev/null
@@ -1,10 +0,0 @@
-{
-    "name": "Sample JSON Strategy 2",
-    "description": "Sample JSON Strategy Test 2",
-    "ta": [
-        {"kind": "ema", "length": 10, "sma": false},
-        {"kind": "sma", "length": 50, "talib": false},
-        {"kind": "kc", "mamode": "hma"},
-        {"kind": "adx", "length": 10}
-    ]
-}
\ No newline at end of file

From fc76649b00b03f1e9784403e86165b5edbb80207 Mon Sep 17 00:00:00 2001
From: "sgmheyhey@gmail.com" <sgmheyhey@gmail.com>
Date: Thu, 9 Dec 2021 05:33:12 +0900
Subject: [PATCH 3/6] fix a simple bug not to get initial ma value correctly

---
 pandas_ta/overlap/smma.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pandas_ta/overlap/smma.py b/pandas_ta/overlap/smma.py
index 9f37804..c1c93cf 100644
--- a/pandas_ta/overlap/smma.py
+++ b/pandas_ta/overlap/smma.py
@@ -57,7 +57,7 @@ def smma(close, length=None, mamode=None, talib=None, offset=None, **kwargs):
     m = close.size
     smma = close.copy()
     smma[:length - 1] = npNaN
-    smma.iloc[length - 1] = ma(mamode, close[0:length], length=length, talib=mode_tal)[-1]
+    smma.iloc[length - 1] = ma(mamode, close[0:length], length=length, talib=mode_tal).iloc[-1]
 
     for i in range(length, m):
         smma.iloc[i] = ((length - 1) * smma.iloc[i - 1] + smma.iloc[i]) / length

From 9aaa6cc9a6ab2f0e593b2cbbac967366f2a441b4 Mon Sep 17 00:00:00 2001
From: Kevin Johnson <appliedmathkj@gmail.com>
Date: Mon, 13 Dec 2021 13:07:31 -0800
Subject: [PATCH 4/6] DOC user contribution added

---
 README.md | 4 ++--
 setup.py  | 2 +-
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index d0a7687..f56adff 100644
--- a/README.md
+++ b/README.md
@@ -118,7 +118,7 @@ $ pip install pandas_ta
 
 Latest Version
 --------------
-Best choice! Version: *0.3.38b*
+Best choice! Version: *0.3.39b*
 * Includes all fixes and updates between **pypi** and what is covered in this README.
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
@@ -219,7 +219,7 @@ Thanks for using **Pandas TA**!
 
 _Thank you for your contributions!_
 
-<a href="https://github.com/AbyssAlora"><img src="https://avatars.githubusercontent.com/u/32155747?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/abmyii"><img src="https://avatars.githubusercontent.com/u/52673001?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/alexonab"><img src="https://avatars.githubusercontent.com/u/16689258?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/allahyarzadeh"><img src="https://avatars.githubusercontent.com/u/11909557?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/anthotsang"><img src="https://avatars.githubusercontent.com/u/929793?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/bizso09"><img src="https://avatars.githubusercontent.com/u/1904536?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/CMobley7"><img src="https://avatars.githubusercontent.com/u/10121829?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/codesutras"><img src="https://avatars.githubusercontent.com/u/56551122?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/DannyMartens"><img src="https://avatars.githubusercontent.com/u/37220423?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/DrPaprikaa"><img src="https://avatars.githubusercontent.com/u/64958936?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/daikts"><img src="https://avatars.githubusercontent.com/u/64799229?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/danlim-wz"><img src="https://avatars.githubusercontent.com/u/52344837?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/delicateear"><img src="https://avatars.githubusercontent.com/u/167213?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/dorren"><img src="https://avatars.githubusercontent.com/u/27552?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/edwardwang1"><img src="https://avatars.githubusercontent.com/u/15675816?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"></a> <a href="https://github.com/FGU1"><img src="https://avatars.githubusercontent.com/u/56175843?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/ffhirata"><img src="https://avatars.githubusercontent.com/u/44292530?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/floatinghotpot"><img src="https://avatars.githubusercontent.com/u/2339512?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/FriendlyUser"><img src="https://avatars.githubusercontent.com/u/13860264?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/GSlinger"><img src="https://avatars.githubusercontent.com/u/24567123?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/hhashim1"><img src="https://avatars.githubusercontent.com/u/62855649?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/jhleong"><img src="https://avatars.githubusercontent.com/u/1499913?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/JoeSchr"><img src="https://avatars.githubusercontent.com/u/8218910?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/Larry-u"><img src="https://avatars.githubusercontent.com/u/18108119?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/lluissalord"><img src="https://avatars.githubusercontent.com/u/7021552?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/locupleto"><img src="https://avatars.githubusercontent.com/u/3994906?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/lucasmlofaro"><img src="https://avatars.githubusercontent.com/u/15791696?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/luisbarrancos"><img src="https://avatars.githubusercontent.com/u/387352?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/M6stafa"><img src="https://avatars.githubusercontent.com/u/7975309?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/maxdignan"><img src="https://avatars.githubusercontent.com/u/8184722?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/mchant"><img src="https://avatars.githubusercontent.com/u/8502845?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/mihakralj"><img src="https://avatars.githubusercontent.com/u/31756078?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/moritzgun"><img src="https://avatars.githubusercontent.com/u/68067719?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/NkosenhleDuma"><img src="https://avatars.githubusercontent.com/u/51145741?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/nicoloridulfo"><img src="https://avatars.githubusercontent.com/u/49532161?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/olafos"><img src="https://avatars.githubusercontent.com/u/2526551?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/pbrumblay"><img src="https://avatars.githubusercontent.com/u/2146159?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/pssolanki111"><img src="https://avatars.githubusercontent.com/u/40393715?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/RajeshDhalange"><img src="https://avatars.githubusercontent.com/u/32175897?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/ramgarg102"><img src="https://avatars.githubusercontent.com/u/47526387?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/rengel8"><img src="https://avatars.githubusercontent.com/u/34138513?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/rluong003"><img src="https://avatars.githubusercontent.com/u/42408939?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/SoftDevDanial"><img src="https://avatars.githubusercontent.com/u/64815604?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/schwaa"><img src="https://avatars.githubusercontent.com/u/2640598?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/tg12"><img src="https://avatars.githubusercontent.com/u/12201893?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/twrobel"><img src="https://avatars.githubusercontent.com/u/394724?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/UncleVasya"><img src="https://avatars.githubusercontent.com/u/1286157?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/WellMaybeItIs"><img src="https://avatars.githubusercontent.com/u/84646494?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/whubsch"><img src="https://avatars.githubusercontent.com/u/24275736?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/witokondoria"><img src="https://avatars.githubusercontent.com/u/5685669?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/wouldayajustlookatit"><img src="https://avatars.githubusercontent.com/u/44936661?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"></a> <a href="https://github.com/YuvalWein"><img src="https://avatars.githubusercontent.com/u/65113623?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/zlpatel"><img src="https://avatars.githubusercontent.com/u/3293739?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a>
+<a href="https://github.com/AbyssAlora"><img src="https://avatars.githubusercontent.com/u/32155747?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/abmyii"><img src="https://avatars.githubusercontent.com/u/52673001?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/alexonab"><img src="https://avatars.githubusercontent.com/u/16689258?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/allahyarzadeh"><img src="https://avatars.githubusercontent.com/u/11909557?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/anthotsang"><img src="https://avatars.githubusercontent.com/u/929793?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/bizso09"><img src="https://avatars.githubusercontent.com/u/1904536?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/CMobley7"><img src="https://avatars.githubusercontent.com/u/10121829?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/codesutras"><img src="https://avatars.githubusercontent.com/u/56551122?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/DannyMartens"><img src="https://avatars.githubusercontent.com/u/37220423?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/DrPaprikaa"><img src="https://avatars.githubusercontent.com/u/64958936?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/daikts"><img src="https://avatars.githubusercontent.com/u/64799229?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/danlim-wz"><img src="https://avatars.githubusercontent.com/u/52344837?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/delicateear"><img src="https://avatars.githubusercontent.com/u/167213?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/dorren"><img src="https://avatars.githubusercontent.com/u/27552?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/edwardwang1"><img src="https://avatars.githubusercontent.com/u/15675816?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"></a> <a href="https://github.com/FGU1"><img src="https://avatars.githubusercontent.com/u/56175843?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/ffhirata"><img src="https://avatars.githubusercontent.com/u/44292530?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/floatinghotpot"><img src="https://avatars.githubusercontent.com/u/2339512?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/FriendlyUser"><img src="https://avatars.githubusercontent.com/u/13860264?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/GSlinger"><img src="https://avatars.githubusercontent.com/u/24567123?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/hhashim1"><img src="https://avatars.githubusercontent.com/u/62855649?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/jhleong"><img src="https://avatars.githubusercontent.com/u/1499913?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/JoeSchr"><img src="https://avatars.githubusercontent.com/u/8218910?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/Larry-u"><img src="https://avatars.githubusercontent.com/u/18108119?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/lluissalord"><img src="https://avatars.githubusercontent.com/u/7021552?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/locupleto"><img src="https://avatars.githubusercontent.com/u/3994906?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/lucasmlofaro"><img src="https://avatars.githubusercontent.com/u/15791696?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/luisbarrancos"><img src="https://avatars.githubusercontent.com/u/387352?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/M6stafa"><img src="https://avatars.githubusercontent.com/u/7975309?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/maxdignan"><img src="https://avatars.githubusercontent.com/u/8184722?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/mchant"><img src="https://avatars.githubusercontent.com/u/8502845?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/mihakralj"><img src="https://avatars.githubusercontent.com/u/31756078?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/moritzgun"><img src="https://avatars.githubusercontent.com/u/68067719?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/NkosenhleDuma"><img src="https://avatars.githubusercontent.com/u/51145741?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/nicoloridulfo"><img src="https://avatars.githubusercontent.com/u/49532161?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/olafos"><img src="https://avatars.githubusercontent.com/u/2526551?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/pbrumblay"><img src="https://avatars.githubusercontent.com/u/2146159?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/pssolanki111"><img src="https://avatars.githubusercontent.com/u/40393715?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/RajeshDhalange"><img src="https://avatars.githubusercontent.com/u/32175897?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/ramgarg102"><img src="https://avatars.githubusercontent.com/u/47526387?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/rengel8"><img src="https://avatars.githubusercontent.com/u/34138513?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/rluong003"><img src="https://avatars.githubusercontent.com/u/42408939?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/sgmheyhey"><img src="https://avatars.githubusercontent.com/u/30946314?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/SoftDevDanial"><img src="https://avatars.githubusercontent.com/u/64815604?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/schwaa"><img src="https://avatars.githubusercontent.com/u/2640598?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/tg12"><img src="https://avatars.githubusercontent.com/u/12201893?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/twrobel"><img src="https://avatars.githubusercontent.com/u/394724?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/UncleVasya"><img src="https://avatars.githubusercontent.com/u/1286157?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/WellMaybeItIs"><img src="https://avatars.githubusercontent.com/u/84646494?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/whubsch"><img src="https://avatars.githubusercontent.com/u/24275736?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/witokondoria"><img src="https://avatars.githubusercontent.com/u/5685669?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/wouldayajustlookatit"><img src="https://avatars.githubusercontent.com/u/44936661?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"></a> <a href="https://github.com/YuvalWein"><img src="https://avatars.githubusercontent.com/u/65113623?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a> <a href="https://github.com/zlpatel"><img src="https://avatars.githubusercontent.com/u/3293739?v=4" class="avatar-user" width="35px;" style="border-radius: 5px;"/></a>
 
 <br/>
 
diff --git a/setup.py b/setup.py
index 5394b2f..6fbcc2d 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "3", "38b")),
+    version=".".join(("0", "3", "39b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

From e55d1c0491d1f4f87f103112e1f71b47ea456420 Mon Sep 17 00:00:00 2001
From: Kevin Johnson <appliedmathkj@gmail.com>
Date: Tue, 14 Dec 2021 15:12:55 -0800
Subject: [PATCH 5/6] DOC ind calc desc dropped TST stoch and wb_tsv MAINT
 wb_tsv added and stoch talib and vp ta ext removed

---
 README.md                               |  9 ++-
 pandas_ta/__init__.py                   |  5 +-
 pandas_ta/candles/cdl_doji.py           | 11 ---
 pandas_ta/candles/cdl_inside.py         |  8 ---
 pandas_ta/candles/cdl_z.py              | 10 ---
 pandas_ta/candles/ha.py                 | 19 -----
 pandas_ta/core.py                       |  5 +-
 pandas_ta/cycles/ebsw.py                |  3 -
 pandas_ta/cycles/reflex.py              |  3 -
 pandas_ta/momentum/ao.py                |  7 --
 pandas_ta/momentum/apo.py               |  6 --
 pandas_ta/momentum/bias.py              |  7 --
 pandas_ta/momentum/bop.py               |  3 -
 pandas_ta/momentum/brar.py              | 14 ----
 pandas_ta/momentum/cci.py               | 10 ---
 pandas_ta/momentum/cfo.py               |  7 --
 pandas_ta/momentum/cg.py                |  4 --
 pandas_ta/momentum/cmo.py               |  7 --
 pandas_ta/momentum/coppock.py           | 10 ---
 pandas_ta/momentum/cti.py               |  4 ++
 pandas_ta/momentum/dm.py                | 20 ++----
 pandas_ta/momentum/er.py                | 10 ---
 pandas_ta/momentum/eri.py               |  8 ---
 pandas_ta/momentum/fisher.py            | 23 ------
 pandas_ta/momentum/inertia.py           |  7 --
 pandas_ta/momentum/kdj.py               | 12 ----
 pandas_ta/momentum/kst.py               | 14 ----
 pandas_ta/momentum/macd.py              | 13 ----
 pandas_ta/momentum/mom.py               |  5 --
 pandas_ta/momentum/pgo.py               |  9 ---
 pandas_ta/momentum/ppo.py               | 11 ---
 pandas_ta/momentum/psl.py               | 14 ----
 pandas_ta/momentum/pvo.py               |  9 ---
 pandas_ta/momentum/qqe.py               |  4 --
 pandas_ta/momentum/roc.py               |  6 --
 pandas_ta/momentum/rsi.py               | 15 ----
 pandas_ta/momentum/rsx.py               |  3 -
 pandas_ta/momentum/rvgi.py              |  8 ---
 pandas_ta/momentum/slope.py             |  6 +-
 pandas_ta/momentum/smi.py               | 10 ---
 pandas_ta/momentum/squeeze.py           | 31 --------
 pandas_ta/momentum/squeeze_pro.py       | 30 --------
 pandas_ta/momentum/stc.py               |  7 --
 pandas_ta/momentum/stoch.py             | 36 +++++-----
 pandas_ta/momentum/stochf.py            | 10 ---
 pandas_ta/momentum/stochrsi.py          | 14 ----
 pandas_ta/momentum/td_seq.py            |  5 --
 pandas_ta/momentum/trix.py              | 10 ---
 pandas_ta/momentum/tsi.py               | 15 ----
 pandas_ta/momentum/uo.py                | 18 -----
 pandas_ta/momentum/willr.py             |  8 ---
 pandas_ta/overlap/alligator.py          |  9 ---
 pandas_ta/overlap/alma.py               |  8 ---
 pandas_ta/overlap/dema.py               |  9 ---
 pandas_ta/overlap/ema.py                |  9 ---
 pandas_ta/overlap/fwma.py               | 12 ----
 pandas_ta/overlap/hilo.py               | 27 -------
 pandas_ta/overlap/hl2.py                |  4 +-
 pandas_ta/overlap/hlc3.py               |  4 +-
 pandas_ta/overlap/hma.py                | 11 ---
 pandas_ta/overlap/hwma.py               |  9 +--
 pandas_ta/overlap/ichimoku.py           | 14 ----
 pandas_ta/overlap/jma.py                |  4 --
 pandas_ta/overlap/kama.py               |  4 --
 pandas_ta/overlap/linreg.py             | 19 -----
 pandas_ta/overlap/mcgd.py               | 15 +---
 pandas_ta/overlap/midpoint.py           |  9 ---
 pandas_ta/overlap/midprice.py           |  9 ---
 pandas_ta/overlap/ohlc4.py              |  4 +-
 pandas_ta/overlap/pwma.py               | 12 ----
 pandas_ta/overlap/rma.py                |  7 --
 pandas_ta/overlap/sinwma.py             | 13 ----
 pandas_ta/overlap/sma.py                |  5 --
 pandas_ta/overlap/smma.py               |  8 ---
 pandas_ta/overlap/ssf.py                |  6 --
 pandas_ta/overlap/supertrend.py         | 25 -------
 pandas_ta/overlap/swma.py               | 12 ----
 pandas_ta/overlap/t3.py                 | 16 -----
 pandas_ta/overlap/tema.py               |  9 ---
 pandas_ta/overlap/trima.py              |  8 ---
 pandas_ta/overlap/vidya.py              |  9 ---
 pandas_ta/overlap/vwap.py               |  5 --
 pandas_ta/overlap/vwma.py               |  7 --
 pandas_ta/overlap/wcp.py                |  3 -
 pandas_ta/overlap/wma.py                | 14 ----
 pandas_ta/overlap/zlma.py               |  9 ---
 pandas_ta/performance/drawdown.py       |  6 --
 pandas_ta/performance/log_return.py     |  6 --
 pandas_ta/performance/percent_return.py |  6 --
 pandas_ta/statistics/entropy.py         |  7 --
 pandas_ta/statistics/kurtosis.py        |  5 --
 pandas_ta/statistics/mad.py             |  5 --
 pandas_ta/statistics/median.py          |  5 --
 pandas_ta/statistics/quantile.py        |  5 --
 pandas_ta/statistics/skew.py            |  5 --
 pandas_ta/statistics/stdev.py           |  6 --
 pandas_ta/statistics/tos_stdevall.py    | 12 ----
 pandas_ta/statistics/variance.py        |  5 --
 pandas_ta/statistics/zscore.py          |  9 ---
 pandas_ta/trend/adx.py                  | 52 +-------------
 pandas_ta/trend/amat.py                 | 13 ----
 pandas_ta/trend/aroon.py                | 15 ----
 pandas_ta/trend/chop.py                 | 10 ---
 pandas_ta/trend/cksp.py                 | 16 ++---
 pandas_ta/trend/decay.py                |  9 ---
 pandas_ta/trend/decreasing.py           |  9 ---
 pandas_ta/trend/dpo.py                  | 10 ---
 pandas_ta/trend/increasing.py           |  9 ---
 pandas_ta/trend/long_run.py             | 12 ----
 pandas_ta/trend/psar.py                 |  6 --
 pandas_ta/trend/qstick.py               |  6 --
 pandas_ta/trend/short_run.py            | 12 ----
 pandas_ta/trend/trendflex.py            |  3 -
 pandas_ta/trend/tsignals.py             |  8 ---
 pandas_ta/trend/ttm_trend.py            | 13 +---
 pandas_ta/trend/vhf.py                  |  8 ---
 pandas_ta/trend/vortex.py               | 14 ----
 pandas_ta/trend/xsignals.py             |  8 ---
 pandas_ta/volatility/accbands.py        | 18 -----
 pandas_ta/volatility/atr.py             | 22 ------
 pandas_ta/volatility/bbands.py          | 18 -----
 pandas_ta/volatility/donchian.py        |  7 --
 pandas_ta/volatility/hwc.py             | 28 +++-----
 pandas_ta/volatility/kc.py              | 22 ------
 pandas_ta/volatility/massi.py           | 11 ---
 pandas_ta/volatility/natr.py            |  6 --
 pandas_ta/volatility/pdist.py           |  6 --
 pandas_ta/volatility/rvi.py             | 14 ----
 pandas_ta/volatility/thermo.py          | 16 -----
 pandas_ta/volatility/true_range.py      |  7 --
 pandas_ta/volatility/ui.py              | 13 ----
 pandas_ta/volume/__init__.py            |  2 +-
 pandas_ta/volume/ad.py                  | 11 ---
 pandas_ta/volume/adosc.py               |  9 ---
 pandas_ta/volume/aobv.py                | 17 -----
 pandas_ta/volume/cmf.py                 | 12 ----
 pandas_ta/volume/efi.py                 | 12 ----
 pandas_ta/volume/eom.py                 | 10 ---
 pandas_ta/volume/kvo.py                 |  9 ---
 pandas_ta/volume/mfi.py                 | 12 ----
 pandas_ta/volume/nvi.py                 | 12 ----
 pandas_ta/volume/obv.py                 |  4 --
 pandas_ta/volume/pvi.py                 | 12 ----
 pandas_ta/volume/pvol.py                |  6 --
 pandas_ta/volume/pvr.py                 |  6 --
 pandas_ta/volume/pvt.py                 |  7 --
 pandas_ta/volume/tv_tsv.py              | 96 -------------------------
 pandas_ta/volume/vp.py                  | 14 ----
 pandas_ta/volume/wb_tsv.py              | 84 ++++++++++++++++++++++
 setup.py                                |  2 +-
 tests/test_ext_indicator_volume.py      |  7 ++
 tests/test_indicator_momentum.py        |  6 +-
 tests/test_indicator_volume.py          |  5 ++
 153 files changed, 166 insertions(+), 1583 deletions(-)
 delete mode 100644 pandas_ta/volume/tv_tsv.py
 create mode 100644 pandas_ta/volume/wb_tsv.py

diff --git a/README.md b/README.md
index f56adff..c948fcb 100644
--- a/README.md
+++ b/README.md
@@ -62,7 +62,7 @@ _Pandas Technical Analysis_ (**Pandas TA**) is an easy to use library that lever
     * [Trend](#trend-19)
     * [Utility](#utility-5)
     * [Volatility](#volatility-14)
-    * [Volume](#volume-15)
+    * [Volume](#volume-16)
 * [Misc](#misc)
     * [Backtesting](#backtesting)
     * [Performance Metrics](#performance-metrics)
@@ -118,7 +118,7 @@ $ pip install pandas_ta
 
 Latest Version
 --------------
-Best choice! Version: *0.3.39b*
+Best choice! Version: *0.3.40b*
 * Includes all fixes and updates between **pypi** and what is covered in this README.
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
@@ -898,7 +898,7 @@ Use parameter: cumulative=**True** for cumulative results.
 
 <br/>
 
-### **Volume** (15)
+### **Volume** (16)
 
 * _Accumulation/Distribution Index_: **ad**
 * _Accumulation/Distribution Oscillator_: **adosc**
@@ -915,6 +915,7 @@ Use parameter: cumulative=**True** for cumulative results.
 * _Price Volume Rank_: **pvr**
 * _Price Volume Trend_: **pvt**
 * _Volume Profile_: **vp**
+* _Worden Brothers Time Segmented Value_: **wb_tsv**
 
 | _On-Balance Volume_ (OBV) |
 |:--------:|
@@ -1025,6 +1026,7 @@ help(ta.sample)
 ## **Breaking / Depreciated Indicators**
 * _Arnaud Legoux Moving Average_ (**alma**) Updated accuracy and speed with new default ```length=9``` and argument ```distribution_offset``` renamed to ```dist_offset```. See ```help(ta.alma)```.
 * _Trend Return_ (**trend_return**) has been removed and replaced with **tsignals**. When given a trend Series like ```close > sma(close, 50)``` it returns the Trend, Trade Entries and Trade Exits of that trend to make it compatible with [**vectorbt**](https://github.com/polakowo/vectorbt) by setting ```asbool=True``` to get boolean Trade Entries and Exits. See ```help(ta.tsignals)```
+* _Volume Profile_ (**vp**) is no longer part of the DataFrame Extension since it does not return a Time Series.
 * _Zero Lag Moving Average_ (**zlma**) now using available Moving Averages from ```ta.ma```. See ```help(ta.zlma)``` and ```help(ta.ma)```.
 
 <br/>
@@ -1039,6 +1041,7 @@ help(ta.sample)
 * _Squeeze Pro_ (**squeeze_pro**) is an extended version of "TTM Squeeze" from John Carter. See ```help(ta.squeeze_pro)```
 * _Tom DeMark's Sequential_ (**td_seq**) attempts to identify a price point where an uptrend or a downtrend exhausts itself and reverses. Currently exlcuded from ```df.ta.strategy()``` for performance reasons. See ```help(ta.td_seq)```
 * _Think or Swim Standard Deviation All_ (**tos_stdevall**) indicator which returns the standard deviation of data for the entire plot or for the interval of the last bars defined by the length parameter. See ```help(ta.tos_stdevall)```
+* _Worden Brothers Time Segmented Value_ (**wb_tsv**) is an oscillator indicator that attempts to indentify money flow in a stock, similar to On Balance Volume (**obv**). See ```help(ta.wb_tsv)```
 
 <br/>
 
diff --git a/pandas_ta/__init__.py b/pandas_ta/__init__.py
index 87cd421..11ad7e4 100644
--- a/pandas_ta/__init__.py
+++ b/pandas_ta/__init__.py
@@ -80,10 +80,11 @@ Category = {
         "natr", "pdist", "rvi", "thermo", "true_range", "ui"
     ],
 
-    # Volume, "vp" or "Volume Profile" is unique
+    # Volume.
+    # Note: "vp" or "Volume Profile" is excluded since it does not return a Time Series
     "volume": [
         "ad", "adosc", "aobv", "cmf", "efi", "eom", "kvo", "mfi", "nvi", "obv",
-        "pvi", "pvol", "pvr", "pvt"
+        "pvi", "pvol", "pvr", "pvt", "wb_tsv"
     ],
 }
 
diff --git a/pandas_ta/candles/cdl_doji.py b/pandas_ta/candles/cdl_doji.py
index 9c053c3..6d41dee 100644
--- a/pandas_ta/candles/cdl_doji.py
+++ b/pandas_ta/candles/cdl_doji.py
@@ -13,17 +13,6 @@ def cdl_doji(open_, high, low, close, length=None, factor=None, scalar=None, asi
     Sources:
         TA-Lib: 96.56% Correlation
 
-    Calculation:
-        Default values:
-            length=10, percent=10 (0.1), scalar=100
-        ABS = Absolute Value
-        SMA = Simple Moving Average
-
-        BODY = ABS(close - open)
-        HL_RANGE = ABS(high - low)
-
-        DOJI = scalar IF BODY < 0.01 * percent * SMA(HL_RANGE, length) ELSE 0
-
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/candles/cdl_inside.py b/pandas_ta/candles/cdl_inside.py
index 272e85e..1ade4c0 100644
--- a/pandas_ta/candles/cdl_inside.py
+++ b/pandas_ta/candles/cdl_inside.py
@@ -15,14 +15,6 @@ def cdl_inside(open_, high, low, close, asbool=False, offset=None, **kwargs):
     Sources:
         https://www.tradingview.com/script/IyIGN1WO-Inside-Bar/
 
-    Calculation:
-        Default Inputs:
-            asbool=False
-        inside = (high.diff() < 0) & (low.diff() > 0)
-
-        if not asbool:
-            inside *= candle_color(open_, close)
-
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/candles/cdl_z.py b/pandas_ta/candles/cdl_z.py
index a003f2b..b15cc4c 100644
--- a/pandas_ta/candles/cdl_z.py
+++ b/pandas_ta/candles/cdl_z.py
@@ -11,16 +11,6 @@ def cdl_z(open_, high, low, close, length=None, full=None, ddof=None, offset=Non
 
     Source: Kevin Johnson
 
-    Calculation:
-        Default values:
-            length=30, full=False, ddof=1
-        Z = ZSCORE
-
-        open  = Z( open, length, ddof)
-        high  = Z( high, length, ddof)
-        low   = Z(  low, length, ddof)
-        close = Z(close, length, ddof)
-
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/candles/ha.py b/pandas_ta/candles/ha.py
index f6b2532..6473a6c 100644
--- a/pandas_ta/candles/ha.py
+++ b/pandas_ta/candles/ha.py
@@ -19,25 +19,6 @@ def ha(open_, high, low, close, offset=None, **kwargs):
     Sources:
         https://www.investopedia.com/terms/h/heikinashi.asp
 
-    Calculation:
-        HA_OPEN[0] = (open[0] + close[0]) / 2
-        HA_CLOSE = (open[0] + high[0] + low[0] + close[0]) / 4
-
-        for i > 1 in df.index:
-            HA_OPEN = (HA_OPEN[i−1] + HA_CLOSE[i−1]) / 2
-
-        HA_HIGH = MAX(HA_OPEN, HA_HIGH, HA_CLOSE)
-        HA_LOW = MIN(HA_OPEN, HA_LOW, HA_CLOSE)
-
-        How to Calculate Heikin-Ashi
-
-        Use one period to create the first Heikin-Ashi (HA) candle, using
-        the formulas. For example use the high, low, open, and close to
-        create the first HA close price. Use the open and close to create
-        the first HA open. The high of the period will be the first HA high,
-        and the low will be the first HA low. With the first HA calculated,
-        it is now possible to continue computing the HA candles per the formulas.
-    ​​
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/core.py b/pandas_ta/core.py
index ba134db..19df776 100644
--- a/pandas_ta/core.py
+++ b/pandas_ta/core.py
@@ -669,7 +669,6 @@ class AnalysisIndicators(BasePandasObject):
             "short_run",
             "td_seq", # Performance exclusion
             "tsignals",
-            "vp",
             "xsignals",
         ]
 
@@ -1804,8 +1803,8 @@ class AnalysisIndicators(BasePandasObject):
         result = pvt(close=close, volume=volume, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
 
-    def vp(self, width=None, percent=None, **kwargs):
+    def wb_tsv(self, length=None, signal=None, offset=None, **kwargs):
         close = self._get_column(kwargs.pop("close", "close"))
         volume = self._get_column(kwargs.pop("volume", "volume"))
-        result = vp(close=close, volume=volume, width=width, percent=percent, **kwargs)
+        result = wb_tsv(close=close, volume=volume, signal=signal, offset=offset, **kwargs)
         return self._post_process(result, **kwargs)
diff --git a/pandas_ta/cycles/ebsw.py b/pandas_ta/cycles/ebsw.py
index 5cad4b7..c7ed2a5 100644
--- a/pandas_ta/cycles/ebsw.py
+++ b/pandas_ta/cycles/ebsw.py
@@ -36,9 +36,6 @@ def ebsw(close, length=None, bars=None, offset=None, initial_version=False, **kw
         - https://www.prorealcode.com/prorealtime-indicators/even-better-sinewave/
         - J.F.Ehlers 'Cycle Analytics for Traders', 2014
 
-    Calculation:
-        refer to 'sources' or implementation
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's max cycle/trend period. Values between 40-48 work like
diff --git a/pandas_ta/cycles/reflex.py b/pandas_ta/cycles/reflex.py
index 63a79c1..c7092e9 100644
--- a/pandas_ta/cycles/reflex.py
+++ b/pandas_ta/cycles/reflex.py
@@ -24,9 +24,6 @@ def reflex(close, length=None, smooth=None, alpha=None, offset=None, **kwargs):
     Sources:
         https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/
 
-    Calculation:
-        Refer to provided source or the code above.
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 20
diff --git a/pandas_ta/momentum/ao.py b/pandas_ta/momentum/ao.py
index e436e1c..8d3bddc 100644
--- a/pandas_ta/momentum/ao.py
+++ b/pandas_ta/momentum/ao.py
@@ -13,13 +13,6 @@ def ao(high, low, fast=None, slow=None, offset=None, **kwargs):
         https://www.tradingview.com/wiki/Awesome_Oscillator_(AO)
         https://www.ifcm.co.uk/ntx-indicators/awesome-oscillator
 
-    Calculation:
-        Default Inputs:
-            fast=5, slow=34
-        SMA = Simple Moving Average
-        median = (high + low) / 2
-        AO = SMA(median, fast) - SMA(median, slow)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/apo.py b/pandas_ta/momentum/apo.py
index 74f9883..4866da8 100644
--- a/pandas_ta/momentum/apo.py
+++ b/pandas_ta/momentum/apo.py
@@ -14,12 +14,6 @@ def apo(close, fast=None, slow=None, mamode=None, talib=None, offset=None, **kwa
     Sources:
         https://www.tradingtechnologies.com/xtrader-help/x-study/technical-indicator-definitions/absolute-price-oscillator-apo/
 
-    Calculation:
-        Default Inputs:
-            fast=12, slow=26
-        SMA = Simple Moving Average
-        APO = SMA(close, fast) - SMA(close, slow)
-
     Args:
         close (pd.Series): Series of 'close's
         fast (int): The short period. Default: 12
diff --git a/pandas_ta/momentum/bias.py b/pandas_ta/momentum/bias.py
index f48bc7e..a70bbc2 100644
--- a/pandas_ta/momentum/bias.py
+++ b/pandas_ta/momentum/bias.py
@@ -12,13 +12,6 @@ def bias(close, length=None, mamode=None, offset=None, **kwargs):
         Few internet resources on definitive definition.
         Request by Github user homily, issue #46
 
-    Calculation:
-        Default Inputs:
-            length=26, MA='sma'
-
-        BIAS = (close - MA(close, length)) / MA(close, length)
-            = (close / MA(close, length)) - 1
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): The period. Default: 26
diff --git a/pandas_ta/momentum/bop.py b/pandas_ta/momentum/bop.py
index 224deb8..51ef086 100644
--- a/pandas_ta/momentum/bop.py
+++ b/pandas_ta/momentum/bop.py
@@ -11,9 +11,6 @@ def bop(open_, high, low, close, scalar=None, talib=None, offset=None, **kwargs)
     Sources:
         http://www.worden.com/TeleChartHelp/Content/Indicators/Balance_of_Power.htm
 
-    Calculation:
-        BOP = scalar * (close - open) / (high - low)
-
     Args:
         open (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/momentum/brar.py b/pandas_ta/momentum/brar.py
index ec601c6..eb8d258 100644
--- a/pandas_ta/momentum/brar.py
+++ b/pandas_ta/momentum/brar.py
@@ -12,20 +12,6 @@ def brar(open_, high, low, close, length=None, scalar=None, drift=None, offset=N
         No internet resources on definitive definition.
         Request by Github user homily, issue #46
 
-    Calculation:
-        Default Inputs:
-            length=26, scalar=100
-        SUM = Sum
-
-        HO_Diff = high - open
-        OL_Diff = open - low
-        HCY = high - close[-1]
-        CYL = close[-1] - low
-        HCY[HCY < 0] = 0
-        CYL[CYL < 0] = 0
-        AR = scalar * SUM(HO, length) / SUM(OL, length)
-        BR = scalar * SUM(HCY, length) / SUM(CYL, length)
-
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/momentum/cci.py b/pandas_ta/momentum/cci.py
index cf62515..b4f5f1c 100644
--- a/pandas_ta/momentum/cci.py
+++ b/pandas_ta/momentum/cci.py
@@ -14,16 +14,6 @@ def cci(high, low, close, length=None, c=None, talib=None, offset=None, **kwargs
     Sources:
         https://www.tradingview.com/wiki/Commodity_Channel_Index_(CCI)
 
-    Calculation:
-        Default Inputs:
-            length=14, c=0.015
-        SMA = Simple Moving Average
-        MAD = Mean Absolute Deviation
-        tp = typical_price = hlc3 = (high + low + close) / 3
-        mean_tp = SMA(tp, length)
-        mad_tp = MAD(tp, length)
-        CCI = (tp - mean_tp) / (c * mad_tp)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/cfo.py b/pandas_ta/momentum/cfo.py
index 94fe4ce..38b9297 100644
--- a/pandas_ta/momentum/cfo.py
+++ b/pandas_ta/momentum/cfo.py
@@ -12,13 +12,6 @@ def cfo(close, length=None, scalar=None, drift=None, offset=None, **kwargs):
     Sources:
         https://www.fmlabs.com/reference/default.htm?url=ForecastOscillator.htm
 
-    Calculation:
-        Default Inputs:
-            length=9, drift=1, scalar=100
-        LINREG = Linear Regression
-
-        CFO = scalar * (close - LINERREG(length, tdf=True)) / close
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): The period. Default: 9
diff --git a/pandas_ta/momentum/cg.py b/pandas_ta/momentum/cg.py
index a63abe4..a6544f4 100644
--- a/pandas_ta/momentum/cg.py
+++ b/pandas_ta/momentum/cg.py
@@ -11,10 +11,6 @@ def cg(close, length=None, offset=None, **kwargs):
     Sources:
         http://www.mesasoftware.com/papers/TheCGOscillator.pdf
 
-    Calculation:
-        Default Inputs:
-            length=10
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): The length of the period. Default: 10
diff --git a/pandas_ta/momentum/cmo.py b/pandas_ta/momentum/cmo.py
index 6893e1c..9b425a4 100644
--- a/pandas_ta/momentum/cmo.py
+++ b/pandas_ta/momentum/cmo.py
@@ -14,13 +14,6 @@ def cmo(close, length=None, scalar=None, talib=None, drift=None, offset=None, **
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/chande-momentum-oscillator-cmo/
         https://www.tradingview.com/script/hdrf0fXV-Variable-Index-Dynamic-Average-VIDYA/
 
-    Calculation:
-        Default Inputs:
-            drift=1, scalar=100
-
-        # Same Calculation as RSI except for this step
-        CMO = scalar * (PSUM - NSUM) / (PSUM + NSUM)
-
     Args:
         close (pd.Series): Series of 'close's
         scalar (float): How much to magnify. Default: 100
diff --git a/pandas_ta/momentum/coppock.py b/pandas_ta/momentum/coppock.py
index 6329437..12f8aa7 100644
--- a/pandas_ta/momentum/coppock.py
+++ b/pandas_ta/momentum/coppock.py
@@ -16,16 +16,6 @@ def coppock(close, length=None, fast=None, slow=None, offset=None, **kwargs):
     Sources:
         https://en.wikipedia.org/wiki/Coppock_curve
 
-    Calculation:
-        Default Inputs:
-            length=10, fast=11, slow=14
-        SMA = Simple Moving Average
-        MAD = Mean Absolute Deviation
-        tp = typical_price = hlc3 = (high + low + close) / 3
-        mean_tp = SMA(tp, length)
-        mad_tp = MAD(tp, length)
-        CCI = (tp - mean_tp) / (c * mad_tp)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): WMA period. Default: 10
diff --git a/pandas_ta/momentum/cti.py b/pandas_ta/momentum/cti.py
index 4d74c01..a55bb28 100644
--- a/pandas_ta/momentum/cti.py
+++ b/pandas_ta/momentum/cti.py
@@ -17,6 +17,10 @@ def cti(close, length=None, offset=None, **kwargs) -> Series:
         length (int): It's period. Default: 12
         offset (int): How many periods to offset the result. Default: 0
 
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value)
+        fill_method (value, optional): Type of fill method
+
     Returns:
         pd.Series: Series of the CTI values for the given period.
     """
diff --git a/pandas_ta/momentum/dm.py b/pandas_ta/momentum/dm.py
index 0ff2701..a3fb986 100644
--- a/pandas_ta/momentum/dm.py
+++ b/pandas_ta/momentum/dm.py
@@ -16,22 +16,6 @@ def dm(high, low, length=None, mamode=None, talib=None, drift=None, offset=None,
         https://www.tradingview.com/pine-script-reference/#fun_dmi
         https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=24&Name=Directional_Movement_Index
 
-    Calculation:
-        Default Inputs:
-            length=14, mamode="rma", drift=1
-                up = high - high.shift(drift)
-            dn = low.shift(drift) - low
-
-            pos_ = ((up > dn) & (up > 0)) * up
-            neg_ = ((dn > up) & (dn > 0)) * dn
-
-            pos_ = pos_.apply(zero)
-            neg_ = neg_.apply(zero)
-
-            # Not the same values as TA Lib's -+DM
-            pos = ma(mamode, pos_, length=length)
-            neg = ma(mamode, neg_, length=length)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
@@ -41,6 +25,10 @@ def dm(high, low, length=None, mamode=None, talib=None, drift=None, offset=None,
         drift (int): The difference period. Default: 1
         offset (int): How many periods to offset the result. Default: 0
 
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value)
+        fill_method (value, optional): Type of fill method
+
     Returns:
         pd.DataFrame: DMP (+DM) and DMN (-DM) columns.
     """
diff --git a/pandas_ta/momentum/er.py b/pandas_ta/momentum/er.py
index 79e9c12..e6bf876 100644
--- a/pandas_ta/momentum/er.py
+++ b/pandas_ta/momentum/er.py
@@ -13,16 +13,6 @@ def er(close, length=None, drift=None, offset=None, **kwargs):
     Sources:
         https://help.tc2000.com/m/69404/l/749623-kaufman-efficiency-ratio
 
-    Calculation:
-        Default Inputs:
-            length=10
-        ABS = Absolute Value
-        EMA = Exponential Moving Average
-
-        abs_diff = ABS(close.diff(length))
-        volatility = ABS(close.diff(1))
-        ER = abs_diff / SUM(volatility, length)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
diff --git a/pandas_ta/momentum/eri.py b/pandas_ta/momentum/eri.py
index 2570ca0..46f3e52 100644
--- a/pandas_ta/momentum/eri.py
+++ b/pandas_ta/momentum/eri.py
@@ -20,14 +20,6 @@ def eri(high, low, close, length=None, offset=None, **kwargs):
     Sources:
         https://admiralmarkets.com/education/articles/forex-indicators/bears-and-bulls-power-indicator
 
-    Calculation:
-        Default Inputs:
-            length=13
-        EMA = Exponential Moving Average
-
-        BULLPOWER = high - EMA(close, length)
-        BEARPOWER = low - EMA(close, length)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/fisher.py b/pandas_ta/momentum/fisher.py
index bd765a2..e6d3e73 100644
--- a/pandas_ta/momentum/fisher.py
+++ b/pandas_ta/momentum/fisher.py
@@ -16,29 +16,6 @@ def fisher(high, low, length=None, signal=None, offset=None, **kwargs):
     Sources:
         TradingView (Correlation >99%)
 
-    Calculation:
-        Default Inputs:
-            length=9, signal=1
-        HL2 = hl2(high, low)
-        HHL2 = HL2.rolling(length).max()
-        LHL2 = HL2.rolling(length).min()
-
-        HLR = HHL2 - LHL2
-        HLR[HLR < 0.001] = 0.001
-
-        position = ((HL2 - LHL2) / HLR) - 0.5
-
-        v = 0
-        m = high.size
-        FISHER = [npNaN for _ in range(0, length - 1)] + [0]
-        for i in range(length, m):
-            v = 0.66 * position[i] + 0.67 * v
-            if v < -0.99: v = -0.999
-            if v >  0.99: v =  0.999
-            FISHER.append(0.5 * (nplog((1 + v) / (1 - v)) + FISHER[i - 1]))
-
-        SIGNAL = FISHER.shift(signal)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/inertia.py b/pandas_ta/momentum/inertia.py
index 2387ce0..372f480 100644
--- a/pandas_ta/momentum/inertia.py
+++ b/pandas_ta/momentum/inertia.py
@@ -15,13 +15,6 @@ def inertia(close=None, high=None, low=None, length=None, rvi_length=None, scala
     Sources:
         https://www.investopedia.com/terms/r/relative_vigor_index.asp
 
-    Calculation:
-        Default Inputs:
-            length=14, ma_length=20
-        LSQRMA = Least Squares Moving Average
-
-        INERTIA = LSQRMA(RVI(length), ma_length)
-
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/momentum/kdj.py b/pandas_ta/momentum/kdj.py
index 991572c..f48ae4a 100644
--- a/pandas_ta/momentum/kdj.py
+++ b/pandas_ta/momentum/kdj.py
@@ -16,18 +16,6 @@ def kdj(high=None, low=None, close=None, length=None, signal=None, offset=None,
         https://www.prorealcode.com/prorealtime-indicators/kdj/
         https://docs.anychart.com/Stock_Charts/Technical_Indicators/Mathematical_Description#kdj
 
-    Calculation:
-        Default Inputs:
-            length=9, signal=3
-        LL = low for last 9 periods
-        HH = high for last 9 periods
-
-        FAST_K = 100 * (close - LL) / (HH - LL)
-
-        K = RMA(FAST_K, signal)
-        D = RMA(K, signal)
-        J = 3K - 2D
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/kst.py b/pandas_ta/momentum/kst.py
index 7a3985e..f38a298 100644
--- a/pandas_ta/momentum/kst.py
+++ b/pandas_ta/momentum/kst.py
@@ -13,20 +13,6 @@ def kst(close, roc1=None, roc2=None, roc3=None, roc4=None, sma1=None, sma2=None,
         https://www.tradingview.com/wiki/Know_Sure_Thing_(KST)
         https://www.incrediblecharts.com/indicators/kst.php
 
-    Calculation:
-        Default Inputs:
-            roc1=10, roc2=15, roc3=20, roc4=30,
-            sma1=10, sma2=10, sma3=10, sma4=15, signal=9, drift=1
-        ROC = Rate of Change
-        SMA = Simple Moving Average
-        rocsma1 = SMA(ROC(close, roc1), sma1)
-        rocsma2 = SMA(ROC(close, roc2), sma2)
-        rocsma3 = SMA(ROC(close, roc3), sma3)
-        rocsma4 = SMA(ROC(close, roc4), sma4)
-
-        KST = 100 * (rocsma1 + 2 * rocsma2 + 3 * rocsma3 + 4 * rocsma4)
-        KST_Signal = SMA(KST, signal)
-
     Args:
         close (pd.Series): Series of 'close's
         roc1 (int): ROC 1 period. Default: 10
diff --git a/pandas_ta/momentum/macd.py b/pandas_ta/momentum/macd.py
index c3fff77..5fb2295 100644
--- a/pandas_ta/momentum/macd.py
+++ b/pandas_ta/momentum/macd.py
@@ -17,19 +17,6 @@ def macd(close, fast=None, slow=None, signal=None, talib=None, offset=None, **kw
         https://www.tradingview.com/wiki/MACD_(Moving_Average_Convergence/Divergence)
         AS Mode: https://tr.tradingview.com/script/YFlKXHnP/
 
-    Calculation:
-        Default Inputs:
-            fast=12, slow=26, signal=9
-        EMA = Exponential Moving Average
-        MACD = EMA(close, fast) - EMA(close, slow)
-        Signal = EMA(MACD, signal)
-        Histogram = MACD - Signal
-
-        if asmode:
-            MACD = MACD - Signal
-            Signal = EMA(MACD, signal)
-            Histogram = MACD - Signal
-
     Args:
         close (pd.Series): Series of 'close's
         fast (int): The short period. Default: 12
diff --git a/pandas_ta/momentum/mom.py b/pandas_ta/momentum/mom.py
index 39fd3fd..3d5d502 100644
--- a/pandas_ta/momentum/mom.py
+++ b/pandas_ta/momentum/mom.py
@@ -12,11 +12,6 @@ def mom(close, length=None, talib=None, offset=None, **kwargs):
     Sources:
         http://www.onlinetradingconcepts.com/TechnicalAnalysis/Momentum.html
 
-    Calculation:
-        Default Inputs:
-            length=1
-        MOM = close.diff(length)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
diff --git a/pandas_ta/momentum/pgo.py b/pandas_ta/momentum/pgo.py
index 1b04840..882b53b 100644
--- a/pandas_ta/momentum/pgo.py
+++ b/pandas_ta/momentum/pgo.py
@@ -14,15 +14,6 @@ def pgo(high, low, close, length=None, offset=None, **kwargs):
     Sources:
         https://library.tradingtechnologies.com/trade/chrt-ti-pretty-good-oscillator.html
 
-    Calculation:
-        Default Inputs:
-            length=14
-        ATR = Average True Range
-        SMA = Simple Moving Average
-        EMA = Exponential Moving Average
-
-        PGO = (close - SMA(close, length)) / EMA(ATR(high, low, close, length), length)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/ppo.py b/pandas_ta/momentum/ppo.py
index 28baf0c..c363812 100644
--- a/pandas_ta/momentum/ppo.py
+++ b/pandas_ta/momentum/ppo.py
@@ -14,17 +14,6 @@ def ppo(close, fast=None, slow=None, signal=None, scalar=None, mamode=None, tali
     Sources:
         https://www.tradingview.com/wiki/MACD_(Moving_Average_Convergence/Divergence)
 
-    Calculation:
-        Default Inputs:
-            fast=12, slow=26
-        SMA = Simple Moving Average
-        EMA = Exponential Moving Average
-        fast_sma = SMA(close, fast)
-        slow_sma = SMA(close, slow)
-        PPO = 100 * (fast_sma - slow_sma) / slow_sma
-        Signal = EMA(PPO, signal)
-        Histogram = PPO - Signal
-
     Args:
         close(pandas.Series): Series of 'close's
         fast(int): The short period. Default: 12
diff --git a/pandas_ta/momentum/psl.py b/pandas_ta/momentum/psl.py
index 82f40d9..d732d31 100644
--- a/pandas_ta/momentum/psl.py
+++ b/pandas_ta/momentum/psl.py
@@ -14,20 +14,6 @@ def psl(close, open_=None, length=None, scalar=None, drift=None, offset=None, **
     Sources:
         https://www.quantshare.com/item-851-psychological-line
 
-    Calculation:
-        Default Inputs:
-            length=12, scalar=100, drift=1
-
-        IF NOT open:
-            DIFF = SIGN(close - close[drift])
-        ELSE:
-            DIFF = SIGN(close - open)
-
-        DIFF.fillna(0)
-        DIFF[DIFF <= 0] = 0
-
-        PSL = scalar * SUM(DIFF, length) / length
-
     Args:
         close (pd.Series): Series of 'close's
         open_ (pd.Series, optional): Series of 'open's
diff --git a/pandas_ta/momentum/pvo.py b/pandas_ta/momentum/pvo.py
index 751b4d5..1239128 100644
--- a/pandas_ta/momentum/pvo.py
+++ b/pandas_ta/momentum/pvo.py
@@ -12,15 +12,6 @@ def pvo(volume, fast=None, slow=None, signal=None, scalar=None, offset=None, **k
     Sources:
         https://www.fmlabs.com/reference/default.htm?url=PVO.htm
 
-    Calculation:
-        Default Inputs:
-            fast=12, slow=26, signal=9
-        EMA = Exponential Moving Average
-
-        PVO = (EMA(volume, fast) - EMA(volume, slow)) / EMA(volume, slow)
-        Signal = EMA(PVO, signal)
-        Histogram = PVO - Signal
-
     Args:
         volume (pd.Series): Series of 'volume's
         fast (int): The short period. Default: 12
diff --git a/pandas_ta/momentum/qqe.py b/pandas_ta/momentum/qqe.py
index 82ac2e4..b730351 100644
--- a/pandas_ta/momentum/qqe.py
+++ b/pandas_ta/momentum/qqe.py
@@ -21,10 +21,6 @@ def qqe(close, length=None, smooth=None, factor=None, mamode=None, drift=None, o
         https://www.tradingpedia.com/forex-trading-indicators/quantitative-qualitative-estimation
         https://www.prorealcode.com/prorealtime-indicators/qqe-quantitative-qualitative-estimation/
 
-    Calculation:
-        Default Inputs:
-            length=14, smooth=5, factor=4.236, mamode="ema", drift=1
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): RSI period. Default: 14
diff --git a/pandas_ta/momentum/roc.py b/pandas_ta/momentum/roc.py
index ac847bf..18b1ae8 100644
--- a/pandas_ta/momentum/roc.py
+++ b/pandas_ta/momentum/roc.py
@@ -14,12 +14,6 @@ def roc(close, length=None, scalar=None, talib=None, offset=None, **kwargs):
     Sources:
         https://www.tradingview.com/wiki/Rate_of_Change_(ROC)
 
-    Calculation:
-        Default Inputs:
-            length=1
-        MOM = Momentum
-        ROC = 100 * MOM(close, length) / close.shift(length)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
diff --git a/pandas_ta/momentum/rsi.py b/pandas_ta/momentum/rsi.py
index 7bf49b0..8a4d208 100644
--- a/pandas_ta/momentum/rsi.py
+++ b/pandas_ta/momentum/rsi.py
@@ -14,21 +14,6 @@ def rsi(close, length=None, scalar=None, talib=None, drift=None, offset=None, **
     Sources:
         https://www.tradingview.com/wiki/Relative_Strength_Index_(RSI)
 
-    Calculation:
-        Default Inputs:
-            length=14, scalar=100, drift=1
-        ABS = Absolute Value
-        RMA = Rolling Moving Average
-
-        diff = close.diff(drift)
-        positive = diff if diff > 0 else 0
-        negative = diff if diff < 0 else 0
-
-        pos_avg = RMA(positive, length)
-        neg_avg = ABS(RMA(negative, length))
-
-        RSI = scalar * pos_avg / (pos_avg + neg_avg)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 14
diff --git a/pandas_ta/momentum/rsx.py b/pandas_ta/momentum/rsx.py
index d735fff..58834b5 100644
--- a/pandas_ta/momentum/rsx.py
+++ b/pandas_ta/momentum/rsx.py
@@ -17,9 +17,6 @@ def rsx(close, length=None, drift=None, offset=None, **kwargs):
         http://www.jurikres.com/catalog1/ms_rsx.htm
         https://www.prorealcode.com/prorealtime-indicators/jurik-rsx/
 
-    Calculation:
-        Refer to the sources above for information as well as code example.
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 14
diff --git a/pandas_ta/momentum/rvgi.py b/pandas_ta/momentum/rvgi.py
index a903a93..064d2a8 100644
--- a/pandas_ta/momentum/rvgi.py
+++ b/pandas_ta/momentum/rvgi.py
@@ -15,14 +15,6 @@ def rvgi(open_, high, low, close, length=None, swma_length=None, offset=None, **
     Sources:
         https://www.investopedia.com/terms/r/relative_vigor_index.asp
 
-    Calculation:
-        Default Inputs:
-            length=14, swma_length=4
-        SWMA = Symmetrically Weighted Moving Average
-        numerator = SUM(SWMA(close - open, swma_length), length)
-        denominator = SUM(SWMA(high - low, swma_length), length)
-        RVGI = numerator / denominator
-
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/momentum/slope.py b/pandas_ta/momentum/slope.py
index 731ca44..b88525a 100644
--- a/pandas_ta/momentum/slope.py
+++ b/pandas_ta/momentum/slope.py
@@ -24,12 +24,12 @@ def slope( close, length=None, as_angle=None, to_degrees=None, vertical=None, of
 
     Args:
         close (pd.Series): Series of 'close's
-        length (int): It's period.  Default: 1
+        length (int): It's period. Default: 1
+        as_angle (value, optional): Converts slope to an angle. Default: False
+        to_degrees (value, optional): Converts slope angle to degrees. Default: False
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
-        as_angle (value, optional): Converts slope to an angle. Default: False
-        to_degrees (value, optional): Converts slope angle to degrees. Default: False
         fillna (value, optional): pd.DataFrame.fillna(value)
         fill_method (value, optional): Type of fill method
 
diff --git a/pandas_ta/momentum/smi.py b/pandas_ta/momentum/smi.py
index ebfa9c5..9e13695 100644
--- a/pandas_ta/momentum/smi.py
+++ b/pandas_ta/momentum/smi.py
@@ -21,16 +21,6 @@ def smi(close, fast=None, slow=None, signal=None, scalar=None, offset=None, **kw
         https://www.tradingview.com/script/Xh5Q0une-SMI-Ergodic-Oscillator/
         https://www.tradingview.com/script/cwrgy4fw-SMIIO/
 
-    Calculation:
-        Default Inputs:
-            fast=5, slow=20, signal=5
-        TSI = True Strength Index
-        EMA = Exponential Moving Average
-
-        ERG = TSI(close, fast, slow)
-        Signal = EMA(ERG, signal)
-        OSC = ERG - Signal
-
     Args:
         close (pd.Series): Series of 'close's
         fast (int): The short period. Default: 5
diff --git a/pandas_ta/momentum/squeeze.py b/pandas_ta/momentum/squeeze.py
index 1f56126..e764b1f 100644
--- a/pandas_ta/momentum/squeeze.py
+++ b/pandas_ta/momentum/squeeze.py
@@ -24,37 +24,6 @@ def squeeze(high, low, close, bb_length=None, bb_std=None, kc_length=None, kc_sc
         https://www.tradingview.com/scripts/lazybear/
         https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/T-U/TTM-Squeeze
 
-    Calculation:
-        Default Inputs:
-            bb_length=20, bb_std=2, kc_length=20, kc_scalar=1.5, mom_length=12,
-            mom_smooth=12, tr=True, lazybear=False,
-        BB = Bollinger Bands
-        KC = Keltner Channels
-        MOM = Momentum
-        SMA = Simple Moving Average
-        EMA = Exponential Moving Average
-        TR = True Range
-
-        RANGE = TR(high, low, close) if using_tr else high - low
-        BB_LOW, BB_MID, BB_HIGH = BB(close, bb_length, std=bb_std)
-        KC_LOW, KC_MID, KC_HIGH = KC(high, low, close, kc_length, kc_scalar, TR)
-
-        if lazybear:
-            HH = high.rolling(kc_length).max()
-            LL = low.rolling(kc_length).min()
-            AVG  = 0.25 * (HH + LL) + 0.5 * KC_MID
-            SQZ = linreg(close - AVG, kc_length)
-        else:
-            MOMO = MOM(close, mom_length)
-            if mamode == "ema":
-                SQZ = EMA(MOMO, mom_smooth)
-            else:
-                SQZ = EMA(momo, mom_smooth)
-
-        SQZ_ON  = (BB_LOW > KC_LOW) and (BB_HIGH < KC_HIGH)
-        SQZ_OFF = (BB_LOW < KC_LOW) and (BB_HIGH > KC_HIGH)
-        NO_SQZ = !SQZ_ON and !SQZ_OFF
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/squeeze_pro.py b/pandas_ta/momentum/squeeze_pro.py
index 2899996..b8f5612 100644
--- a/pandas_ta/momentum/squeeze_pro.py
+++ b/pandas_ta/momentum/squeeze_pro.py
@@ -24,36 +24,6 @@ def squeeze_pro(high, low, close, bb_length=None, bb_std=None, kc_length=None, k
         https://usethinkscript.com/threads/john-carters-squeeze-pro-indicator-for-thinkorswim-free.4021/
         https://www.tradingview.com/script/TAAt6eRX-Squeeze-PRO-Indicator-Makit0/
 
-    Calculation:
-        Default Inputs:
-            bb_length=20, bb_std=2, kc_length=20, kc_scalar_wide=2,
-            kc_scalar_normal=1.5, kc_scalar_narrow=1, mom_length=12,
-            mom_smooth=6, tr=True,
-        BB = Bollinger Bands
-        KC = Keltner Channels
-        MOM = Momentum
-        SMA = Simple Moving Average
-        EMA = Exponential Moving Average
-        TR = True Range
-
-        RANGE = TR(high, low, close) if using_tr else high - low
-        BB_LOW, BB_MID, BB_HIGH = BB(close, bb_length, std=bb_std)
-        KC_LOW_WIDE, KC_MID_WIDE, KC_HIGH_WIDE = KC(high, low, close, kc_length, kc_scalar_wide, TR)
-        KC_LOW_NORMAL, KC_MID_NORMAL, KC_HIGH_NORMAL = KC(high, low, close, kc_length, kc_scalar_normal, TR)
-        KC_LOW_NARROW, KC_MID_NARROW, KC_HIGH_NARROW = KC(high, low, close, kc_length, kc_scalar_narrow, TR)
-
-        MOMO = MOM(close, mom_length)
-        if mamode == "ema":
-            SQZPRO = EMA(MOMO, mom_smooth)
-        else:
-            SQZPRO = EMA(momo, mom_smooth)
-
-        SQZPRO_ON_WIDE  = (BB_LOW > KC_LOW_WIDE) and (BB_HIGH < KC_HIGH_WIDE)
-        SQZPRO_ON_NORMAL  = (BB_LOW > KC_LOW_NORMAL) and (BB_HIGH < KC_HIGH_NORMAL)
-        SQZPRO_ON_NARROW  = (BB_LOW > KC_LOW_NARROW) and (BB_HIGH < KC_HIGH_NARROW)
-        SQZPRO_OFF_WIDE = (BB_LOW < KC_LOW_WIDE) and (BB_HIGH > KC_HIGH_WIDE)
-        SQZPRO_NO = !SQZ_ON_WIDE and !SQZ_OFF_WIDE
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/stc.py b/pandas_ta/momentum/stc.py
index 5ec975a..0a0d6b1 100644
--- a/pandas_ta/momentum/stc.py
+++ b/pandas_ta/momentum/stc.py
@@ -26,17 +26,10 @@ def stc(close, tclength=None, fast=None, slow=None, factor=None, offset=None, **
 
     The same goes for osc=, which allows the input of an externally calculated oscillator, overriding ma1 & ma2.
 
-
     Sources:
         Implemented by rengel8 based on work found here:
         https://www.prorealcode.com/prorealtime-indicators/schaff-trend-cycle2/
 
-    Calculation:
-        STCmacd = Moving Average Convergance/Divergance or Oscillator
-        STCstoch = Intermediate Stochastic of MACD/Osc.
-        2nd Stochastic including filtering with results in the
-        STC = Schaff Trend Cycle
-
     Args:
         close (pd.Series): Series of 'close's, used for indexing Series, mandatory
         tclen (int): SchaffTC Signal-Line length.  Default: 10 (adjust to the half of cycle)
diff --git a/pandas_ta/momentum/stoch.py b/pandas_ta/momentum/stoch.py
index c20df25..e83537d 100644
--- a/pandas_ta/momentum/stoch.py
+++ b/pandas_ta/momentum/stoch.py
@@ -1,10 +1,11 @@
 # -*- coding: utf-8 -*-
 from pandas import DataFrame
+from pandas_ta import Imports
 from pandas_ta.overlap import ma
-from pandas_ta.utils import get_offset, non_zero_range, verify_series
+from pandas_ta.utils import get_offset, non_zero_range, tal_ma, verify_series
 
 
-def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=None, **kwargs):
+def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, talib=None, offset=None, **kwargs):
     """Stochastic (STOCH)
 
     The Stochastic Oscillator (STOCH) was developed by George Lane in the 1950's.
@@ -20,17 +21,6 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=N
         https://www.tradingview.com/wiki/Stochastic_(STOCH)
         https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=332&Name=KD_-_Slow
 
-    Calculation:
-        Default Inputs:
-            k=14, d=3, smooth_k=3
-        SMA = Simple Moving Average
-        LL  = low for last k periods
-        HH  = high for last k periods
-
-        STOCH = 100 * (close - LL) / (HH - LL)
-        STOCHk = SMA(STOCH, smooth_k)
-        STOCHd = SMA(FASTK, d)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
@@ -39,6 +29,8 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=N
         d (int): The Slow %D period. Default: 3
         smooth_k (int): The Slow %K period. Default: 3
         mamode (str): See ```help(ta.ma)```. Default: 'sma'
+        talib (bool): If TA Lib is installed and talib is True, Returns the TA Lib
+            version. Default: True
         offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
@@ -58,18 +50,24 @@ def stoch(high, low, close, k=None, d=None, smooth_k=None, mamode=None, offset=N
     close = verify_series(close, _length)
     offset = get_offset(offset)
     mamode = mamode if isinstance(mamode, str) else "sma"
+    mode_tal = bool(talib) if isinstance(talib, bool) else True
 
     if high is None or low is None or close is None: return
 
     # Calculate Result
-    lowest_low = low.rolling(k).min()
-    highest_high = high.rolling(k).max()
+    if Imports["talib"] and mode_tal:
+        from talib import STOCH
+        stoch_ = STOCH(high, low, close, k, d, tal_ma(mamode), d, tal_ma(mamode))
+        stoch_k, stoch_d = stoch_[0], stoch_[1]
+    else:
+        lowest_low = low.rolling(k).min()
+        highest_high = high.rolling(k).max()
 
-    stoch = 100 * (close - lowest_low)
-    stoch /= non_zero_range(highest_high, lowest_low)
+        stoch = 100 * (close - lowest_low)
+        stoch /= non_zero_range(highest_high, lowest_low)
 
-    stoch_k = ma(mamode, stoch.loc[stoch.first_valid_index():,], length=smooth_k)
-    stoch_d = ma(mamode, stoch_k.loc[stoch_k.first_valid_index():,], length=d)
+        stoch_k = ma(mamode, stoch.loc[stoch.first_valid_index():,], length=smooth_k)
+        stoch_d = ma(mamode, stoch_k.loc[stoch_k.first_valid_index():,], length=d)
 
     # Offset
     if offset != 0:
diff --git a/pandas_ta/momentum/stochf.py b/pandas_ta/momentum/stochf.py
index 40cdbd9..f5e536e 100644
--- a/pandas_ta/momentum/stochf.py
+++ b/pandas_ta/momentum/stochf.py
@@ -16,16 +16,6 @@ def stochf(high, low, close, k=None, d=None, mamode=None, talib=None, offset=Non
         https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=333&Name=KD_-_Fast
         https://corporatefinanceinstitute.com/resources/knowledge/trading-investing/fast-stochastic-indicator/
 
-    Calculation:
-        Default Inputs:
-            k=14, d=3, mamode="sma",
-        SMA = Simple Moving Average
-        LL  = low for last k periods
-        HH  = high for last k periods
-
-        STOCHFk = 100 * (close - LL) / (HH - LL)
-        STOCHFd = MA(SMA, STOCH, d)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/stochrsi.py b/pandas_ta/momentum/stochrsi.py
index c5fe145..eb545a3 100644
--- a/pandas_ta/momentum/stochrsi.py
+++ b/pandas_ta/momentum/stochrsi.py
@@ -18,20 +18,6 @@ def stochrsi(close, length=None, rsi_length=None, k=None, d=None, mamode=None, o
     Sources:
         https://www.tradingview.com/wiki/Stochastic_(STOCH)
 
-    Calculation:
-        Default Inputs:
-            length=14, rsi_length=14, k=3, d=3
-        RSI = Relative Strength Index
-        SMA = Simple Moving Average
-
-        RSI = RSI(high, low, close, rsi_length)
-        LL  = lowest RSI for last rsi_length periods
-        HH  = highest RSI for last rsi_length periods
-
-        STOCHRSI  = 100 * (RSI - LL) / (HH - LL)
-        STOCHRSIk = SMA(STOCHRSI, k)
-        STOCHRSId = SMA(STOCHRSIk, d)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/td_seq.py b/pandas_ta/momentum/td_seq.py
index 0c0d9bc..de1babe 100644
--- a/pandas_ta/momentum/td_seq.py
+++ b/pandas_ta/momentum/td_seq.py
@@ -14,11 +14,6 @@ def td_seq(close, asint=None, offset=None, **kwargs):
     Sources:
         https://tradetrekker.wordpress.com/tdsequential/
 
-    Calculation:
-        Compare current close price with 4 days ago price, up to 13 days. For the
-        consecutive ascending or descending price sequence, display 6th to 9th day
-        value.
-
     Args:
         close (pd.Series): Series of 'close's
         asint (bool): If True, fillnas with 0 and change type to int. Default: False
diff --git a/pandas_ta/momentum/trix.py b/pandas_ta/momentum/trix.py
index 90d7978..e4a0dbe 100644
--- a/pandas_ta/momentum/trix.py
+++ b/pandas_ta/momentum/trix.py
@@ -12,16 +12,6 @@ def trix(close, length=None, signal=None, scalar=None, drift=None, offset=None,
     Sources:
         https://www.tradingview.com/wiki/TRIX
 
-    Calculation:
-        Default Inputs:
-            length=18, drift=1
-        EMA = Exponential Moving Average
-        ROC = Rate of Change
-        ema1 = EMA(close, length)
-        ema2 = EMA(ema1, length)
-        ema3 = EMA(ema2, length)
-        TRIX = 100 * ROC(ema3, drift)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 18
diff --git a/pandas_ta/momentum/tsi.py b/pandas_ta/momentum/tsi.py
index 8d7c366..46fddeb 100644
--- a/pandas_ta/momentum/tsi.py
+++ b/pandas_ta/momentum/tsi.py
@@ -14,21 +14,6 @@ def tsi(close, fast=None, slow=None, signal=None, scalar=None, mamode=None, drif
     Sources:
         https://www.investopedia.com/terms/t/tsi.asp
 
-    Calculation:
-        Default Inputs:
-            fast=13, slow=25, signal=13, scalar=100, drift=1
-        EMA = Exponential Moving Average
-        diff = close.diff(drift)
-
-        slow_ema = EMA(diff, slow)
-        fast_slow_ema = EMA(slow_ema, slow)
-
-        abs_diff_slow_ema = absolute_diff_ema = EMA(ABS(diff), slow)
-        abema = abs_diff_fast_slow_ema = EMA(abs_diff_slow_ema, fast)
-
-        TSI = scalar * fast_slow_ema / abema
-        Signal = EMA(TSI, signal)
-
     Args:
         close (pd.Series): Series of 'close's
         fast (int): The short period. Default: 13
diff --git a/pandas_ta/momentum/uo.py b/pandas_ta/momentum/uo.py
index f224d71..9b9fbbd 100644
--- a/pandas_ta/momentum/uo.py
+++ b/pandas_ta/momentum/uo.py
@@ -13,24 +13,6 @@ def uo(high, low, close, fast=None, medium=None, slow=None, fast_w=None, medium_
     Sources:
         https://www.tradingview.com/wiki/Ultimate_Oscillator_(UO)
 
-    Calculation:
-        Default Inputs:
-            fast=7, medium=14, slow=28,
-            fast_w=4.0, medium_w=2.0, slow_w=1.0, drift=1
-        min_low_or_pc  = close.shift(drift).combine(low, min)
-        max_high_or_pc = close.shift(drift).combine(high, max)
-
-        bp = buying pressure = close - min_low_or_pc
-        tr = true range = max_high_or_pc - min_low_or_pc
-
-        fast_avg = SUM(bp, fast) / SUM(tr, fast)
-        medium_avg = SUM(bp, medium) / SUM(tr, medium)
-        slow_avg = SUM(bp, slow) / SUM(tr, slow)
-
-        total_weight = fast_w + medium_w + slow_w
-        weights = (fast_w * fast_avg) + (medium_w * medium_avg) + (slow_w * slow_avg)
-        UO = 100 * weights / total_weight
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/momentum/willr.py b/pandas_ta/momentum/willr.py
index 98f2140..38eaa57 100644
--- a/pandas_ta/momentum/willr.py
+++ b/pandas_ta/momentum/willr.py
@@ -12,14 +12,6 @@ def willr(high, low, close, length=None, talib=None, offset=None, **kwargs):
     Sources:
         https://www.tradingview.com/wiki/Williams_%25R_(%25R)
 
-    Calculation:
-        Default Inputs:
-            length=20
-        LL = low.rolling(length).min()
-        HH = high.rolling(length).max()
-
-        WILLR = 100 * ((close - LL) / (HH - LL) - 1)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/overlap/alligator.py b/pandas_ta/overlap/alligator.py
index 5a14e9c..2ed3d4e 100644
--- a/pandas_ta/overlap/alligator.py
+++ b/pandas_ta/overlap/alligator.py
@@ -20,15 +20,6 @@ def alligator(close, jaw=None, teeth=None, lips=None, talib=None, offset=None, *
         https://www.tradingview.com/scripts/alligator/
         https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=175&Name=Bill_Williams_Alligator
 
-    Calculation:
-        Default Inputs:
-            jaw=13, teeth=8, lips=5, mamode="sma"
-        SMMA = SMoothed Moving Average
-
-        JAW = SMMA(close, jaw)
-        TEETH = SMMA(close, teeth)
-        LIPS = SMMA(close, lips)
-
     Args:
         close (pd.Series): Series of 'close's
         jaw (int): The Jaw period. Default: 13
diff --git a/pandas_ta/overlap/alma.py b/pandas_ta/overlap/alma.py
index 9a8dc24..72690c1 100644
--- a/pandas_ta/overlap/alma.py
+++ b/pandas_ta/overlap/alma.py
@@ -24,14 +24,6 @@ def alma(close, length=None, sigma=None, dist_offset=None, offset=None, **kwargs
         https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=475&Name=Moving_Average_-_Arnaud_Legoux
         https://www.prorealcode.com/prorealtime-indicators/alma-arnaud-legoux-moving-average/
 
-    Calculation:
-        length=9, sigma=6.0, dist_offset=0.85
-
-        X = [0, 1, ..., length - 1]
-        WEIGHTS = e^(-0.5 * (X - FLOOR(dist_offset(length - 1)))^2 / (length / sigma)^2)
-
-        ALMA = close.rolling(length).apply(WEIGHTS)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period, window size. Default: 9
diff --git a/pandas_ta/overlap/dema.py b/pandas_ta/overlap/dema.py
index fa45a62..149e8f1 100644
--- a/pandas_ta/overlap/dema.py
+++ b/pandas_ta/overlap/dema.py
@@ -13,15 +13,6 @@ def dema(close, length=None, talib=None, offset=None, **kwargs):
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/double-exponential-moving-average-dema/
 
-    Calculation:
-        Default Inputs:
-            length=10
-        EMA = Exponential Moving Average
-        ema1 = EMA(close, length)
-        ema2 = EMA(ema1, length)
-
-        DEMA = 2 * ema1 - ema2
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/ema.py b/pandas_ta/overlap/ema.py
index 4ba812d..70682ef 100644
--- a/pandas_ta/overlap/ema.py
+++ b/pandas_ta/overlap/ema.py
@@ -17,15 +17,6 @@ def ema(close, length=None, talib=None, offset=None, **kwargs):
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:moving_averages
         https://www.investopedia.com/ask/answers/122314/what-exponential-moving-average-ema-formula-and-how-ema-calculated.asp
 
-    Calculation:
-        Default Inputs:
-            length=10, adjust=False, sma=True
-        if sma:
-            sma_nth = close[0:length].sum() / length
-            close[:length - 1] = np.NaN
-            close.iloc[length - 1] = sma_nth
-        EMA = close.ewm(span=length, adjust=adjust).mean()
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/fwma.py b/pandas_ta/overlap/fwma.py
index 161b0cb..cdd7733 100644
--- a/pandas_ta/overlap/fwma.py
+++ b/pandas_ta/overlap/fwma.py
@@ -10,18 +10,6 @@ def fwma(close, length=None, asc=None, offset=None, **kwargs):
 
     Source: Kevin Johnson
 
-    Calculation:
-        Default Inputs:
-            length=10,
-
-        def weights(w):
-            def _compute(x):
-                return np.dot(w * x)
-            return _compute
-
-        fibs = utils.fibonacci(length - 1)
-        FWMA = close.rolling(length)_.apply(weights(fibs), raw=True)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/hilo.py b/pandas_ta/overlap/hilo.py
index 5bfc7da..c467442 100644
--- a/pandas_ta/overlap/hilo.py
+++ b/pandas_ta/overlap/hilo.py
@@ -23,33 +23,6 @@ def hilo(high, low, close, high_length=None, low_length=None, mamode=None, offse
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
         https://www.tradingview.com/script/XNQSLIYb-Gann-High-Low/
 
-    Calculation:
-        Default Inputs:
-            high_length=13, low_length=21, mamode="sma"
-        EMA = Exponential Moving Average
-        HMA = Hull Moving Average
-        SMA = Simple Moving Average # Default
-
-        if "ema":
-            high_ma = EMA(high, high_length)
-            low_ma = EMA(low, low_length)
-        elif "hma":
-            high_ma = HMA(high, high_length)
-            low_ma = HMA(low, low_length)
-        else: # "sma"
-            high_ma = SMA(high, high_length)
-            low_ma = SMA(low, low_length)
-
-        # Similar to Supertrend MA selection
-        hilo = Series(npNaN, index=close.index)
-        for i in range(1, m):
-            if close.iloc[i] > high_ma.iloc[i - 1]:
-                hilo.iloc[i] = low_ma.iloc[i]
-            elif close.iloc[i] < low_ma.iloc[i - 1]:
-                hilo.iloc[i] = high_ma.iloc[i]
-            else:
-                hilo.iloc[i] = hilo.iloc[i - 1]
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/overlap/hl2.py b/pandas_ta/overlap/hl2.py
index d6383c9..a5236e9 100644
--- a/pandas_ta/overlap/hl2.py
+++ b/pandas_ta/overlap/hl2.py
@@ -5,12 +5,12 @@ from pandas_ta.utils import get_offset, verify_series
 def hl2(high, low, offset=None, **kwargs):
     """HL2
 
-    Calculation:
-        HL2 = 0.5 * (high + low)
+    HL2 is the midpoint/average of high and low.
 
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
+        offset (int): How many periods to offset the result. Default: 0
 
     Returns:
         pd.Series: New feature generated.
diff --git a/pandas_ta/overlap/hlc3.py b/pandas_ta/overlap/hlc3.py
index 20558f1..4c3b241 100644
--- a/pandas_ta/overlap/hlc3.py
+++ b/pandas_ta/overlap/hlc3.py
@@ -6,13 +6,13 @@ from pandas_ta.utils import get_offset, verify_series
 def hlc3(high, low, close, talib=None, offset=None, **kwargs):
     """HLC3
 
-    Calculation:
-        HLC3 = (high + low + close) / 3.0
+    HLC3 is the average of high, low and close.
 
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
         close (pd.Series): Series of 'close's
+        offset (int): How many periods to offset the result. Default: 0
 
     Returns:
         pd.Series: New feature generated.
diff --git a/pandas_ta/overlap/hma.py b/pandas_ta/overlap/hma.py
index 0b7f8c5..05413bd 100644
--- a/pandas_ta/overlap/hma.py
+++ b/pandas_ta/overlap/hma.py
@@ -13,17 +13,6 @@ def hma(close, length=None, offset=None, **kwargs):
     Sources:
         https://alanhull.com/hull-moving-average
 
-    Calculation:
-        Default Inputs:
-            length=10
-        WMA = Weighted Moving Average
-        half_length = int(0.5 * length)
-        sqrt_length = int(sqrt(length))
-
-        wmaf = WMA(close, half_length)
-        wmas = WMA(close, length)
-        HMA = WMA(2 * wmaf - wmas, sqrt_length)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/hwma.py b/pandas_ta/overlap/hwma.py
index 0616c19..a03f769 100644
--- a/pandas_ta/overlap/hwma.py
+++ b/pandas_ta/overlap/hwma.py
@@ -16,19 +16,12 @@ def hwma(close, na=None, nb=None, nc=None, offset=None, **kwargs):
     Sources:
         https://www.mql5.com/en/code/20856
 
-    Calculation:
-        HWMA[i] = F[i] + V[i] + 0.5 * A[i]
-        where..
-        F[i] = (1-na) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + na * Price[i]
-        V[i] = (1-nb) * (V[i-1] + A[i-1]) + nb * (F[i] - F[i-1])
-        A[i] = (1-nc) * A[i-1] + nc * (V[i] - V[i-1])
-
     Args:
         close (pd.Series): Series of 'close's
         na (float): Smoothed series parameter (from 0 to 1). Default: 0.2
         nb (float): Trend parameter (from 0 to 1). Default: 0.1
         nc (float): Seasonality parameter (from 0 to 1). Default: 0.1
-        close (pd.Series): Series of 'close's
+        offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
diff --git a/pandas_ta/overlap/ichimoku.py b/pandas_ta/overlap/ichimoku.py
index 2aec18c..f0cc4c1 100644
--- a/pandas_ta/overlap/ichimoku.py
+++ b/pandas_ta/overlap/ichimoku.py
@@ -12,20 +12,6 @@ def ichimoku(high, low, close, tenkan=None, kijun=None, senkou=None, include_chi
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/ichimoku-ich/
 
-    Calculation:
-        Default Inputs:
-            tenkan=9, kijun=26, senkou=52
-        MIDPRICE = Midprice
-        TENKAN_SEN = MIDPRICE(high, low, close, length=tenkan)
-        KIJUN_SEN = MIDPRICE(high, low, close, length=kijun)
-        CHIKOU_SPAN = close.shift(-kijun)
-
-        SPAN_A = 0.5 * (TENKAN_SEN + KIJUN_SEN)
-        SPAN_A = SPAN_A.shift(kijun)
-
-        SPAN_B = MIDPRICE(high, low, close, length=senkou)
-        SPAN_B = SPAN_B.shift(kijun)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/overlap/jma.py b/pandas_ta/overlap/jma.py
index e20f8f5..680e1fa 100644
--- a/pandas_ta/overlap/jma.py
+++ b/pandas_ta/overlap/jma.py
@@ -20,10 +20,6 @@ def jma(close, length=None, phase=None, offset=None, **kwargs):
         https://c.mql5.com/forextsd/forum/164/jurik_1.pdf
         https://www.prorealcode.com/prorealtime-indicators/jurik-volatility-bands/
 
-    Calculation:
-        Default Inputs:
-            length=7, phase=0
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): Period of calculation. Default: 7
diff --git a/pandas_ta/overlap/kama.py b/pandas_ta/overlap/kama.py
index 009616d..aa75b40 100644
--- a/pandas_ta/overlap/kama.py
+++ b/pandas_ta/overlap/kama.py
@@ -18,10 +18,6 @@ def kama(close, length=None, fast=None, slow=None, mamode=None, drift=None, offs
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:kaufman_s_adaptive_moving_average
         https://www.tradingview.com/script/wZGOIz9r-REPOST-Indicators-3-Different-Adaptive-Moving-Averages/
 
-    Calculation:
-        Default Inputs:
-            length=10
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/linreg.py b/pandas_ta/overlap/linreg.py
index 58e61ad..f673ef2 100644
--- a/pandas_ta/overlap/linreg.py
+++ b/pandas_ta/overlap/linreg.py
@@ -18,25 +18,6 @@ def linreg(close, length=None, talib=None, offset=None, **kwargs):
 
     Source: TA Lib
 
-    Calculation:
-        Default Inputs:
-            length=14
-        x = [1, 2, ..., n]
-        x_sum = 0.5 * length * (length + 1)
-        x2_sum = length * (length + 1) * (2 * length + 1) / 6
-        divisor = length * x2_sum - x_sum * x_sum
-
-        lr(series):
-            y_sum = series.sum()
-            y2_sum = (series* series).sum()
-            xy_sum = (x * series).sum()
-
-            m = (length * xy_sum - x_sum * y_sum) / divisor
-            b = (y_sum * x2_sum - x_sum * xy_sum) / divisor
-            return m * (length - 1) + b
-
-        linreg = close.rolling(length).apply(lr)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/mcgd.py b/pandas_ta/overlap/mcgd.py
index 54f63f6..8607314 100644
--- a/pandas_ta/overlap/mcgd.py
+++ b/pandas_ta/overlap/mcgd.py
@@ -16,24 +16,11 @@ def mcgd(close, length=None, offset=None, c=None, **kwargs):
     Sources:
         https://www.investopedia.com/articles/forex/09/mcginley-dynamic-indicator.asp
 
-    Calculation:
-        Default Inputs:
-            length=10
-            offset=0
-            c=1
-
-        def mcg_(series):
-            denom = (constant * length * (series.iloc[1] / series.iloc[0]) ** 4)
-            series.iloc[1] = (series.iloc[0] + ((series.iloc[1] - series.iloc[0]) / denom))
-            return series.iloc[1]
-        mcg_cell = close[0:].rolling(2, min_periods=2).apply(mcg_, raw=False)
-        mcg_ds = close[:1].append(mcg_cell[1:])
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): Indicator's period. Default: 10
-        offset (int): Number of periods to offset the result. Default: 0
         c (float): Multiplier for the denominator, sometimes set to 0.6. Default: 1
+        offset (int): Number of periods to offset the result. Default: 0
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
diff --git a/pandas_ta/overlap/midpoint.py b/pandas_ta/overlap/midpoint.py
index f432f8b..dd678d0 100644
--- a/pandas_ta/overlap/midpoint.py
+++ b/pandas_ta/overlap/midpoint.py
@@ -8,15 +8,6 @@ def midpoint(close, length=None, talib=None, offset=None, **kwargs):
 
     The Midpoint is the average of the rolling high and low of period length.
 
-    Sources:
-
-    Calculation:
-        Default Inputs:
-            length=2
-        HC = close.rolling(length).max()
-        LC = close.rolling(length).min()
-        MID = 0.5 * (HC + LC)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 2
diff --git a/pandas_ta/overlap/midprice.py b/pandas_ta/overlap/midprice.py
index 4531b5c..08d1aff 100644
--- a/pandas_ta/overlap/midprice.py
+++ b/pandas_ta/overlap/midprice.py
@@ -8,15 +8,6 @@ def midprice(high, low, length=None, talib=None, offset=None, **kwargs):
 
     The Midprice is the average of the rolling high and low of period length.
 
-    Sources:
-
-    Calculation:
-        Default Inputs:
-            length=2
-        HH = high.rolling(length).max()
-        LL = low.rolling(length).min()
-        MID = 0.5 * (HH + LL)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/overlap/ohlc4.py b/pandas_ta/overlap/ohlc4.py
index 75ca8fc..02495aa 100644
--- a/pandas_ta/overlap/ohlc4.py
+++ b/pandas_ta/overlap/ohlc4.py
@@ -5,14 +5,14 @@ from pandas_ta.utils import get_offset, verify_series
 def ohlc4(open_, high, low, close, offset=None, **kwargs):
     """OHLC4
 
-    Calculation:
-        OHLC4 = 0.25 * (open + high + low + close)
+    OHLC4 is the average of open, high, low and close.
 
     Args:
         open_ (pd.Series): Series of 'open's
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
         close (pd.Series): Series of 'close's
+        offset (int): How many periods to offset the result. Default: 0
 
     Returns:
         pd.Series: New feature generated.
diff --git a/pandas_ta/overlap/pwma.py b/pandas_ta/overlap/pwma.py
index 6f33786..c0fe60e 100644
--- a/pandas_ta/overlap/pwma.py
+++ b/pandas_ta/overlap/pwma.py
@@ -10,18 +10,6 @@ def pwma(close, length=None, asc=None, offset=None, **kwargs):
 
     Source: Kevin Johnson
 
-    Calculation:
-        Default Inputs:
-            length=10
-
-        def weights(w):
-            def _compute(x):
-                return np.dot(w * x)
-            return _compute
-
-        triangle = utils.pascals_triangle(length + 1)
-        PWMA = close.rolling(length)_.apply(weights(triangle), raw=True)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period.  Default: 10
diff --git a/pandas_ta/overlap/rma.py b/pandas_ta/overlap/rma.py
index 1cdeb0f..0e1e5fb 100644
--- a/pandas_ta/overlap/rma.py
+++ b/pandas_ta/overlap/rma.py
@@ -12,13 +12,6 @@ def rma(close, length=None, offset=None, **kwargs):
         https://tlc.thinkorswim.com/center/reference/Tech-Indicators/studies-library/V-Z/WildersSmoothing
         https://www.incrediblecharts.com/indicators/wilder_moving_average.php
 
-    Calculation:
-        Default Inputs:
-            length=10
-        EMA = Exponential Moving Average
-        alpha = 1 / length
-        RMA = EMA(close, alpha=alpha)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/sinwma.py b/pandas_ta/overlap/sinwma.py
index c8ee00a..f7a50bd 100644
--- a/pandas_ta/overlap/sinwma.py
+++ b/pandas_ta/overlap/sinwma.py
@@ -15,19 +15,6 @@ def sinwma(close, length=None, offset=None, **kwargs):
         https://www.tradingview.com/script/6MWFvnPO-Sine-Weighted-Moving-Average/
         Author: Everget (https://www.tradingview.com/u/everget/)
 
-    Calculation:
-        Default Inputs:
-            length=10
-
-        def weights(w):
-            def _compute(x):
-                return np.dot(w * x)
-            return _compute
-
-        sines = Series([sin((i + 1) * pi / (length + 1)) for i in range(0, length)])
-        w = sines / sines.sum()
-        SINWMA = close.rolling(length, min_periods=length).apply(weights(w), raw=True)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/sma.py b/pandas_ta/overlap/sma.py
index a6a9dcc..59381f7 100644
--- a/pandas_ta/overlap/sma.py
+++ b/pandas_ta/overlap/sma.py
@@ -12,11 +12,6 @@ def sma(close, length=None, talib=None, offset=None, **kwargs):
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/simple-moving-average-sma/
 
-    Calculation:
-        Default Inputs:
-            length=10
-        SMA = SUM(close, length) / length
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/smma.py b/pandas_ta/overlap/smma.py
index c1c93cf..1c6f3cc 100644
--- a/pandas_ta/overlap/smma.py
+++ b/pandas_ta/overlap/smma.py
@@ -20,14 +20,6 @@ def smma(close, length=None, mamode=None, talib=None, offset=None, **kwargs):
         https://www.tradingview.com/scripts/smma/
         https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=173&Name=Moving_Average_-_Smoothed
 
-    Calculation:
-        Default Inputs:
-            length=10, mamode="sma"
-        MA = Moving Average
-
-        SMMA[0:length] = MA(mamode, close, length)
-        SMMA[:length] = ((length - 1) * SMMA[i] + close[i]) / length
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/ssf.py b/pandas_ta/overlap/ssf.py
index e13995e..5707b74 100644
--- a/pandas_ta/overlap/ssf.py
+++ b/pandas_ta/overlap/ssf.py
@@ -24,12 +24,6 @@ def ssf(close, length=None, poles=None, offset=None, **kwargs):
         https://www.mql5.com/en/code/588
         https://www.mql5.com/en/code/589
 
-    Calculation:
-        Default Inputs:
-            length=10, poles=[2, 3]
-
-        See the source code or Sources listed above.
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/supertrend.py b/pandas_ta/overlap/supertrend.py
index 5be59f5..d192eca 100644
--- a/pandas_ta/overlap/supertrend.py
+++ b/pandas_ta/overlap/supertrend.py
@@ -16,31 +16,6 @@ def supertrend(high, low, close, length=None, multiplier=None, offset=None, **kw
     Sources:
         http://www.freebsensetips.com/blog/detail/7/What-is-supertrend-indicator-its-calculation
 
-    Calculation:
-        Default Inputs:
-            length=7, multiplier=3.0
-        Default Direction:
-        Set to +1 or bullish trend at start
-
-        MID = multiplier * ATR
-        LOWERBAND = HL2 - MID
-        UPPERBAND = HL2 + MID
-
-        if UPPERBAND[i] < FINAL_UPPERBAND[i-1] and close[i-1] > FINAL_UPPERBAND[i-1]:
-            FINAL_UPPERBAND[i] = UPPERBAND[i]
-        else:
-            FINAL_UPPERBAND[i] = FINAL_UPPERBAND[i-1])
-
-        if LOWERBAND[i] > FINAL_LOWERBAND[i-1] and close[i-1] < FINAL_LOWERBAND[i-1]:
-            FINAL_LOWERBAND[i] = LOWERBAND[i]
-        else:
-            FINAL_LOWERBAND[i] = FINAL_LOWERBAND[i-1])
-
-        if close[i] <= FINAL_UPPERBAND[i]:
-            SUPERTREND[i] = FINAL_UPPERBAND[i]
-        else:
-            SUPERTREND[i] = FINAL_LOWERBAND[i]
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/overlap/swma.py b/pandas_ta/overlap/swma.py
index f0ebf23..91d32bb 100644
--- a/pandas_ta/overlap/swma.py
+++ b/pandas_ta/overlap/swma.py
@@ -13,18 +13,6 @@ def swma(close, length=None, asc=None, offset=None, **kwargs):
     Source:
         https://www.tradingview.com/study-script-reference/#fun_swma
 
-    Calculation:
-        Default Inputs:
-            length=10
-
-        def weights(w):
-            def _compute(x):
-                return np.dot(w * x)
-            return _compute
-
-        triangle = utils.symmetric_triangle(length - 1)
-        SWMA = close.rolling(length)_.apply(weights(triangle), raw=True)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/t3.py b/pandas_ta/overlap/t3.py
index c16c170..e37f157 100644
--- a/pandas_ta/overlap/t3.py
+++ b/pandas_ta/overlap/t3.py
@@ -13,22 +13,6 @@ def t3(close, length=None, a=None, talib=None, offset=None, **kwargs):
     Sources:
         http://www.binarytribune.com/forex-trading-indicators/t3-moving-average-indicator/
 
-    Calculation:
-        Default Inputs:
-            length=10, a=0.7
-        c1 = -a^3
-        c2 = 3a^2 + 3a^3 = 3a^2 * (1 + a)
-        c3 = -6a^2 - 3a - 3a^3
-        c4 = a^3 + 3a^2 + 3a + 1
-
-        ema1 = EMA(close, length)
-        ema2 = EMA(ema1, length)
-        ema3 = EMA(ema2, length)
-        ema4 = EMA(ema3, length)
-        ema5 = EMA(ema4, length)
-        ema6 = EMA(ema5, length)
-        T3 = c1 * ema6 + c2 * ema5 + c3 * ema4 + c4 * ema3
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/tema.py b/pandas_ta/overlap/tema.py
index d963e08..10b3096 100644
--- a/pandas_ta/overlap/tema.py
+++ b/pandas_ta/overlap/tema.py
@@ -12,15 +12,6 @@ def tema(close, length=None, talib=None, offset=None, **kwargs):
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/triple-exponential-moving-average-tema/
 
-    Calculation:
-        Default Inputs:
-            length=10
-        EMA = Exponential Moving Average
-        ema1 = EMA(close, length)
-        ema2 = EMA(ema1, length)
-        ema3 = EMA(ema2, length)
-        TEMA = 3 * (ema1 - ema2) + ema3
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/trima.py b/pandas_ta/overlap/trima.py
index b364c9a..76f0bdc 100644
--- a/pandas_ta/overlap/trima.py
+++ b/pandas_ta/overlap/trima.py
@@ -15,14 +15,6 @@ def trima(close, length=None, talib=None, offset=None, **kwargs):
         tma = sma(sma(src, ceil(length / 2)), floor(length / 2) + 1)  # Tradingview
         trima = sma(sma(x, n), n)  # Tradingview
 
-    Calculation:
-        Default Inputs:
-            length=10
-        SMA = Simple Moving Average
-        half_length = round(0.5 * (length + 1))
-        SMA1 = SMA(close, half_length)
-        TRIMA = SMA(SMA1, half_length)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/vidya.py b/pandas_ta/overlap/vidya.py
index b529614..0c0273a 100644
--- a/pandas_ta/overlap/vidya.py
+++ b/pandas_ta/overlap/vidya.py
@@ -17,15 +17,6 @@ def vidya(close, length=None, drift=None, offset=None, **kwargs):
         https://www.tradingview.com/script/hdrf0fXV-Variable-Index-Dynamic-Average-VIDYA/
         https://www.perfecttrendsystem.com/blog_mt4_2/en/vidya-indicator-for-mt4
 
-    Calculation:
-        Default Inputs:
-            length=10, adjust=False, sma=True
-        if sma:
-            sma_nth = close[0:length].sum() / length
-            close[:length - 1] = np.NaN
-            close.iloc[length - 1] = sma_nth
-        EMA = close.ewm(span=length, adjust=adjust).mean()
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 14
diff --git a/pandas_ta/overlap/vwap.py b/pandas_ta/overlap/vwap.py
index cce8d14..591a378 100644
--- a/pandas_ta/overlap/vwap.py
+++ b/pandas_ta/overlap/vwap.py
@@ -14,11 +14,6 @@ def vwap(high, low, close, volume, anchor=None, offset=None, **kwargs):
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/volume-weighted-average-price-vwap/
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vwap_intraday
 
-    Calculation:
-        tp = typical_price = hlc3(high, low, close)
-        tpv = tp * volume
-        VWAP = tpv.cumsum() / volume.cumsum()
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/overlap/vwma.py b/pandas_ta/overlap/vwma.py
index 7da8115..205fa7c 100644
--- a/pandas_ta/overlap/vwma.py
+++ b/pandas_ta/overlap/vwma.py
@@ -11,13 +11,6 @@ def vwma(close, volume, length=None, offset=None, **kwargs):
     Sources:
         https://www.motivewave.com/studies/volume_weighted_moving_average.htm
 
-    Calculation:
-        Default Inputs:
-            length=10
-        SMA = Simple Moving Average
-        pv = close * volume
-        VWMA = SMA(pv, length) / SMA(volume, length)
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/overlap/wcp.py b/pandas_ta/overlap/wcp.py
index 72ba149..a234eeb 100644
--- a/pandas_ta/overlap/wcp.py
+++ b/pandas_ta/overlap/wcp.py
@@ -12,9 +12,6 @@ def wcp(high, low, close, talib=None, offset=None, **kwargs):
     Sources:
         https://www.fmlabs.com/reference/default.htm?url=WeightedCloses.htm
 
-    Calculation:
-        WCP = (2 * close + high + low) / 4
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/overlap/wma.py b/pandas_ta/overlap/wma.py
index 2bf00ba..b77a70a 100644
--- a/pandas_ta/overlap/wma.py
+++ b/pandas_ta/overlap/wma.py
@@ -13,20 +13,6 @@ def wma(close, length=None, asc=None, talib=None, offset=None, **kwargs):
     Sources:
         https://en.wikipedia.org/wiki/Moving_average#Weighted_moving_average
 
-    Calculation:
-        Default Inputs:
-            length=10, asc=True
-        total_weight = 0.5 * length * (length + 1)
-        weights_ = [1, 2, ..., length + 1]  # Ascending
-        weights = weights if asc else weights[::-1]
-
-        def linear_weights(w):
-            def _compute(x):
-                return (w * x).sum() / total_weight
-            return _compute
-
-        WMA = close.rolling(length)_.apply(linear_weights(weights), raw=True)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/overlap/zlma.py b/pandas_ta/overlap/zlma.py
index a370d13..a2a0c83 100644
--- a/pandas_ta/overlap/zlma.py
+++ b/pandas_ta/overlap/zlma.py
@@ -15,15 +15,6 @@ def zlma(close, length=None, mamode=None, offset=None, **kwargs):
     Sources:
         https://en.wikipedia.org/wiki/Zero_lag_exponential_moving_average
 
-    Calculation:
-        Default Inputs:
-            length=10, mamode=EMA
-        EMA = Exponential Moving Average
-        lag = int(0.5 * (length - 1))
-
-        SOURCE = 2 * close - close.shift(lag)
-        ZLMA = MA(kind=mamode, SOURCE, length)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/performance/drawdown.py b/pandas_ta/performance/drawdown.py
index ef6b385..021da08 100644
--- a/pandas_ta/performance/drawdown.py
+++ b/pandas_ta/performance/drawdown.py
@@ -15,12 +15,6 @@ def drawdown(close, offset=None, **kwargs) -> DataFrame:
     Sources:
         https://www.investopedia.com/terms/d/drawdown.asp
 
-    Calculation:
-        PEAKDD = close.cummax()
-        DD = PEAKDD - close
-        DD% = 1 - (close / PEAKDD)
-        DDlog = log(PEAKDD / close)
-
     Args:
         close (pd.Series): Series of 'close's.
         offset (int): How many periods to offset the result. Default: 0
diff --git a/pandas_ta/performance/log_return.py b/pandas_ta/performance/log_return.py
index a2b9fe4..9c125d7 100644
--- a/pandas_ta/performance/log_return.py
+++ b/pandas_ta/performance/log_return.py
@@ -12,12 +12,6 @@ def log_return(close, length=None, cumulative=None, offset=None, **kwargs):
     Sources:
         https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe
 
-    Calculation:
-        Default Inputs:
-            length=1, cumulative=False
-        LOGRET = log( close.diff(periods=length) )
-        CUMLOGRET = LOGRET.cumsum() if cumulative
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 20
diff --git a/pandas_ta/performance/percent_return.py b/pandas_ta/performance/percent_return.py
index 0ab8369..864a909 100644
--- a/pandas_ta/performance/percent_return.py
+++ b/pandas_ta/performance/percent_return.py
@@ -11,12 +11,6 @@ def percent_return(close, length=None, cumulative=None, offset=None, **kwargs):
     Sources:
         https://stackoverflow.com/questions/31287552/logarithmic-returns-in-pandas-dataframe
 
-    Calculation:
-        Default Inputs:
-            length=1, cumulative=False
-        PCTRET = close.pct_change(length)
-        CUMPCTRET = PCTRET.cumsum() if cumulative
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 20
diff --git a/pandas_ta/statistics/entropy.py b/pandas_ta/statistics/entropy.py
index 11c74a0..0fa515a 100644
--- a/pandas_ta/statistics/entropy.py
+++ b/pandas_ta/statistics/entropy.py
@@ -13,13 +13,6 @@ def entropy(close, length=None, base=None, offset=None, **kwargs):
     Sources:
         https://en.wikipedia.org/wiki/Entropy_(information_theory)
 
-    Calculation:
-        Default Inputs:
-            length=10, base=2
-
-        P = close / SUM(close, length)
-        E = SUM(-P * npLog(P) / npLog(base), length)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 10
diff --git a/pandas_ta/statistics/kurtosis.py b/pandas_ta/statistics/kurtosis.py
index d5641f8..a269c46 100644
--- a/pandas_ta/statistics/kurtosis.py
+++ b/pandas_ta/statistics/kurtosis.py
@@ -7,11 +7,6 @@ def kurtosis(close, length=None, offset=None, **kwargs):
 
     Calculates the Kurtosis over a rolling period.
 
-    Calculation:
-        Default Inputs:
-            length=30
-        KURTOSIS = close.rolling(length).kurt()
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/statistics/mad.py b/pandas_ta/statistics/mad.py
index b23462e..57c9035 100644
--- a/pandas_ta/statistics/mad.py
+++ b/pandas_ta/statistics/mad.py
@@ -8,11 +8,6 @@ def mad(close, length=None, offset=None, **kwargs):
 
     Calculates the Mean Absolute Deviation over a rolling period.
 
-    Calculation:
-        Default Inputs:
-            length=30
-        mad = close.rolling(length).mad()
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/statistics/median.py b/pandas_ta/statistics/median.py
index e650bfd..f9757ba 100644
--- a/pandas_ta/statistics/median.py
+++ b/pandas_ta/statistics/median.py
@@ -10,11 +10,6 @@ def median(close, length=None, offset=None, **kwargs):
     Sources:
         https://www.incrediblecharts.com/indicators/median_price.php
 
-    Calculation:
-        Default Inputs:
-            length=30
-        MEDIAN = close.rolling(length).median()
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/statistics/quantile.py b/pandas_ta/statistics/quantile.py
index fd01af2..3d11ad7 100644
--- a/pandas_ta/statistics/quantile.py
+++ b/pandas_ta/statistics/quantile.py
@@ -7,11 +7,6 @@ def quantile(close, length=None, q=None, offset=None, **kwargs):
 
     Calculates the Quantile over a rolling period.
 
-    Calculation:
-        Default Inputs:
-            length=30, q=0.5
-        QUANTILE = close.rolling(length).quantile(q)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/statistics/skew.py b/pandas_ta/statistics/skew.py
index 4af7b37..f89e50f 100644
--- a/pandas_ta/statistics/skew.py
+++ b/pandas_ta/statistics/skew.py
@@ -7,11 +7,6 @@ def skew(close, length=None, offset=None, **kwargs):
 
     Calculates the Skew over a rolling period.
 
-    Calculation:
-        Default Inputs:
-            length=30
-        SKEW = close.rolling(length).skew()
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/statistics/stdev.py b/pandas_ta/statistics/stdev.py
index 013144a..1bc1d52 100644
--- a/pandas_ta/statistics/stdev.py
+++ b/pandas_ta/statistics/stdev.py
@@ -10,12 +10,6 @@ def stdev(close, length=None, ddof=None, talib=None, offset=None, **kwargs):
 
     Calculates the Standard Deviation over a rolling period.
 
-    Calculation:
-        Default Inputs:
-            length=30
-        VAR = Variance
-        STDEV = variance(close, length).apply(np.sqrt)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/statistics/tos_stdevall.py b/pandas_ta/statistics/tos_stdevall.py
index 796e729..64432f6 100644
--- a/pandas_ta/statistics/tos_stdevall.py
+++ b/pandas_ta/statistics/tos_stdevall.py
@@ -17,18 +17,6 @@ def tos_stdevall(close, length=None, stds=None, ddof=None, offset=None, **kwargs
     Sources:
         https://tlc.thinkorswim.com/center/reference/thinkScript/Functions/Statistical/StDevAll
 
-    Calculation:
-        Default Inputs:
-            length=None (All), stds=[1, 2, 3], ddof=1
-        LR = Linear Regression
-        STDEV = Standard Deviation
-
-        LR = LR(close, length)
-        STDEV = STDEV(close, length, ddof)
-        for level in stds:
-            LOWER = LR - level * STDEV
-            UPPER = LR + level * STDEV
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): Bars from current bar. Default: None
diff --git a/pandas_ta/statistics/variance.py b/pandas_ta/statistics/variance.py
index 1216f2d..c4443e6 100644
--- a/pandas_ta/statistics/variance.py
+++ b/pandas_ta/statistics/variance.py
@@ -8,11 +8,6 @@ def variance(close, length=None, ddof=None, talib=None, offset=None, **kwargs):
 
     Calculates the Variance over a rolling period.
 
-    Calculation:
-        Default Inputs:
-            length=30
-        VARIANCE = close.rolling(length).var()
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/statistics/zscore.py b/pandas_ta/statistics/zscore.py
index 9dbb9c2..c432a29 100644
--- a/pandas_ta/statistics/zscore.py
+++ b/pandas_ta/statistics/zscore.py
@@ -9,15 +9,6 @@ def zscore(close, length=None, std=None, offset=None, **kwargs):
 
     Calculates the Z Score over a rolling period.
 
-    Calculation:
-        Default Inputs:
-            length=30, std=1
-        SMA = Simple Moving Average
-        STDEV = Standard Deviation
-        std = std * STDEV(close, length)
-        mean = SMA(close, length)
-        ZSCORE = (close - mean) / std
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 30
diff --git a/pandas_ta/trend/adx.py b/pandas_ta/trend/adx.py
index ee78eaa..df0943c 100644
--- a/pandas_ta/trend/adx.py
+++ b/pandas_ta/trend/adx.py
@@ -12,58 +12,8 @@ def adx(high, low, close, length=None, lensig=None, scalar=None, mamode=None, dr
     the amount of movement in a single direction.
 
     Sources:
-        https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/average-directional-movement-adx/
         TA Lib Correlation: >99%
-
-    Calculation:
-        DMI ADX TREND 2.0 by @TraderR0BERT, NETWORTHIE.COM
-            //Created by @TraderR0BERT, NETWORTHIE.COM, last updated 01/26/2016
-            //DMI Indicator
-            //Resolution input option for higher/lower time frames
-            study(title="DMI ADX TREND 2.0", shorttitle="ADX TREND 2.0")
-
-            adxlen = input(14, title="ADX Smoothing")
-            dilen = input(14, title="DI Length")
-            thold = input(20, title="Threshold")
-
-            threshold = thold
-
-            //Script for Indicator
-            dirmov(len) =>
-                up = change(high)
-                down = -change(low)
-                truerange = rma(tr, len)
-                plus = fixnan(100 * rma(up > down and up > 0 ? up : 0, len) / truerange)
-                minus = fixnan(100 * rma(down > up and down > 0 ? down : 0, len) / truerange)
-                [plus, minus]
-
-            adx(dilen, adxlen) =>
-                [plus, minus] = dirmov(dilen)
-                sum = plus + minus
-                adx = 100 * rma(abs(plus - minus) / (sum == 0 ? 1 : sum), adxlen)
-                [adx, plus, minus]
-
-            [sig, up, down] = adx(dilen, adxlen)
-            osob=input(40,title="Exhaustion Level for ADX, default = 40")
-            col = sig >= sig[1] ? green : sig <= sig[1] ? red : gray
-
-            //Plot Definitions Current Timeframe
-            p1 = plot(sig, color=col, linewidth = 3, title="ADX")
-            p2 = plot(sig, color=col, style=circles, linewidth=3, title="ADX")
-            p3 = plot(up, color=blue, linewidth = 3, title="+DI")
-            p4 = plot(up, color=blue, style=circles, linewidth=3, title="+DI")
-            p5 = plot(down, color=fuchsia, linewidth = 3, title="-DI")
-            p6 = plot(down, color=fuchsia, style=circles, linewidth=3, title="-DI")
-            h1 = plot(threshold, color=black, linewidth =3, title="Threshold")
-
-            trender = (sig >= up or sig >= down) ? 1 : 0
-            bgcolor(trender>0?black:gray, transp=85)
-
-            //Alert Function for ADX crossing Threshold
-            Up_Cross = crossover(up, threshold)
-            alertcondition(Up_Cross, title="DMI+ cross", message="DMI+ Crossing Threshold")
-            Down_Cross = crossover(down, threshold)
-            alertcondition(Down_Cross, title="DMI- cross", message="DMI- Crossing Threshold")
+        https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/average-directional-movement-adx/
 
     Args:
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/trend/amat.py b/pandas_ta/trend/amat.py
index fb040e8..77c33e4 100644
--- a/pandas_ta/trend/amat.py
+++ b/pandas_ta/trend/amat.py
@@ -18,19 +18,6 @@ def amat(close=None, fast=None, slow=None, lookback=None, mamode=None, offset=No
     Sources:
         https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/
 
-    Calculation:
-        Default Inputs:
-            fast=8, slow=21, mamode="ema", lookback=2
-        OBV = On Balance Volume
-        LR = Long Run Trend
-        SR = Short Run Trend
-
-        FMA = ma(close, mamode, fast)
-        SMA = ma(close, mamode, slow)
-
-        AMAT_LR = LR(FMA, SMA, lookback)
-        AMAT_SR = SR(FMA, SMA, lookback)
-
     Args:
         close (pd.Series): Series of 'close's
         fast (int): The period of the fast moving average. Default: 8
diff --git a/pandas_ta/trend/aroon.py b/pandas_ta/trend/aroon.py
index 4265583..28066b3 100644
--- a/pandas_ta/trend/aroon.py
+++ b/pandas_ta/trend/aroon.py
@@ -14,21 +14,6 @@ def aroon(high, low, length=None, scalar=None, talib=None, offset=None, **kwargs
         https://www.tradingview.com/wiki/Aroon
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/aroon-ar/
 
-    Calculation:
-        Default Inputs:
-            length=1, scalar=100
-
-        recent_maximum_index(x): return int(np.argmax(x[::-1]))
-        recent_minimum_index(x): return int(np.argmin(x[::-1]))
-
-        periods_from_hh = high.rolling(length + 1).apply(recent_maximum_index, raw=True)
-        AROON_UP = scalar * (1 - (periods_from_hh / length))
-
-        periods_from_ll = low.rolling(length + 1).apply(recent_minimum_index, raw=True)
-        AROON_DN = scalar * (1 - (periods_from_ll / length))
-
-        AROON_OSC = AROON_UP - AROON_DN
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 14
diff --git a/pandas_ta/trend/chop.py b/pandas_ta/trend/chop.py
index db7fd48..6cc0798 100644
--- a/pandas_ta/trend/chop.py
+++ b/pandas_ta/trend/chop.py
@@ -18,16 +18,6 @@ def chop(high, low, close, length=None, atr_length=None, ln=None, scalar=None, d
         https://www.tradingview.com/scripts/choppinessindex/
         https://www.motivewave.com/studies/choppiness_index.htm
 
-    Calculation:
-        Default Inputs:
-            length=14, scalar=100, drift=1
-        HH = high.rolling(length).max()
-        LL = low.rolling(length).min()
-
-        ATR_SUM = SUM(ATR(drift), length)
-        CHOP = scalar * (LOG10(ATR_SUM) - LOG10(HH - LL))
-        CHOP /= LOG10(length)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/trend/cksp.py b/pandas_ta/trend/cksp.py
index ec19ac0..65de6a7 100644
--- a/pandas_ta/trend/cksp.py
+++ b/pandas_ta/trend/cksp.py
@@ -16,21 +16,14 @@ def cksp(high, low, close, p=None, x=None, q=None, tvmode=None, offset=None, **k
     view implementation uses the Welles Wilder moving average, the book uses a
     simple moving average.
 
+    Defaults:
+    Book:         p=10, x=3, q=20
+    Trading View: p=10, x=1, q=9
+
     Sources:
         https://www.multicharts.com/discussion/viewtopic.php?t=48914
         "The New Technical Trader", Wikey 1st ed. ISBN 9780471597803, page 95
 
-    Calculation:
-        Default Inputs:
-            p=10, x=1, q=9, tvmode=True
-        ATR = Average True Range
-
-        LS0 = high.rolling(p).max() - x * ATR(length=p)
-        LS = LS0.rolling(q).max()
-
-        SS0 = high.rolling(p).min() + x * ATR(length=p)
-        SS = SS0.rolling(q).min()
-
     Args:
         close (pd.Series): Series of 'close's
         p (int): ATR and first stop period. Default: 10 in both modes
@@ -47,7 +40,6 @@ def cksp(high, low, close, p=None, x=None, q=None, tvmode=None, offset=None, **k
         pd.DataFrame: long and short columns.
     """
     # Validate Arguments
-    # TV defaults=(10,1,9), book defaults = (10,3,20)
     p = int(p) if p and p > 0 else 10
     x = float(x) if x and x > 0 else 1 if tvmode is True else 3
     q = int(q) if q and q > 0 else 9 if tvmode is True else 20
diff --git a/pandas_ta/trend/decay.py b/pandas_ta/trend/decay.py
index 8191f5b..fe20e7e 100644
--- a/pandas_ta/trend/decay.py
+++ b/pandas_ta/trend/decay.py
@@ -13,15 +13,6 @@ def decay(close, kind=None, length=None, mode=None, offset=None, **kwargs):
     Sources:
         https://tulipindicators.org/decay
 
-    Calculation:
-        Default Inputs:
-            length=5, mode=None
-
-        if mode == "exponential" or mode == "exp":
-            max(close, close[-1] - exp(-length), 0)
-        else:
-            max(close, close[-1] - (1 / length), 0)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
diff --git a/pandas_ta/trend/decreasing.py b/pandas_ta/trend/decreasing.py
index 5860d74..b9a1b4c 100644
--- a/pandas_ta/trend/decreasing.py
+++ b/pandas_ta/trend/decreasing.py
@@ -9,15 +9,6 @@ def decreasing(close, length=None, strict=None, asint=None, percent=None, drift=
     over the period. When using the kwarg 'asint', then it returns 1 for True
     or 0 for False.
 
-    Calculation:
-        if strict:
-            decreasing = all(i > j for i, j in zip(close[-length:], close[1:]))
-        else:
-            decreasing = close.diff(length) < 0
-
-        if asint:
-            decreasing = decreasing.astype(int)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
diff --git a/pandas_ta/trend/dpo.py b/pandas_ta/trend/dpo.py
index 2a06673..1a9b106 100644
--- a/pandas_ta/trend/dpo.py
+++ b/pandas_ta/trend/dpo.py
@@ -14,16 +14,6 @@ def dpo(close, length=None, centered=True, offset=None, **kwargs):
         https://www.fidelity.com/learning-center/trading-investing/technical-analysis/technical-indicator-guide/dpo
         http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:detrended_price_osci
 
-    Calculation:
-        Default Inputs:
-            length=20, centered=True
-        SMA = Simple Moving Average
-        t = int(0.5 * length) + 1
-
-        DPO = close.shift(t) - SMA(close, length)
-        if centered:
-            DPO = DPO.shift(-t)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
diff --git a/pandas_ta/trend/increasing.py b/pandas_ta/trend/increasing.py
index d4aa916..bbbb691 100644
--- a/pandas_ta/trend/increasing.py
+++ b/pandas_ta/trend/increasing.py
@@ -9,15 +9,6 @@ def increasing(close, length=None, strict=None, asint=None, percent=None, drift=
     over the period. When using the kwarg 'asint', then it returns 1 for True
     or 0 for False.
 
-    Calculation:
-        if strict:
-            increasing = all(i < j for i, j in zip(close[-length:], close[1:]))
-        else:
-            increasing = close.diff(length) > 0
-
-        if asint:
-            increasing = increasing.astype(int)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 1
diff --git a/pandas_ta/trend/long_run.py b/pandas_ta/trend/long_run.py
index 96798c9..9e13343 100644
--- a/pandas_ta/trend/long_run.py
+++ b/pandas_ta/trend/long_run.py
@@ -21,18 +21,6 @@ def long_run(fast, slow, length=None, offset=None, **kwargs):
     It is part of the Converging and Diverging Conditional logic in:
         https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/
 
-    Calculation:
-        Default Inputs:
-            length=2
-        INC = increasing
-        DEC = decreasing
-        BINC = Both increasing
-        PBOT = Potential Bottom
-
-        BINC = INC(fast, length) & INC(slow, length)
-        PBOT = INC(fast, length) & DEC(slow, length)
-        LR = BINC | PBOT
-
     Args:
         fast (pd.Series): Series of 'fast' values.
         slow (pd.Series): Series of 'slow' values.
diff --git a/pandas_ta/trend/psar.py b/pandas_ta/trend/psar.py
index e14c191..f617038 100644
--- a/pandas_ta/trend/psar.py
+++ b/pandas_ta/trend/psar.py
@@ -21,12 +21,6 @@ def psar(high, low, close=None, af0=None, af=None, max_af=None, offset=None, **k
         https://www.tradingview.com/pine-script-reference/#fun_sar
         https://www.sierrachart.com/index.php?page=doc/StudiesReference.php&ID=66&Name=Parabolic
 
-    Calculation:
-        Default Inputs:
-            af0=0.02, af=0.02, max_af=0.2
-
-        See Source links
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/trend/qstick.py b/pandas_ta/trend/qstick.py
index 8d2f970..37cd46b 100644
--- a/pandas_ta/trend/qstick.py
+++ b/pandas_ta/trend/qstick.py
@@ -12,12 +12,6 @@ def qstick(open_, close, length=None, offset=None, **kwargs):
     Sources:
         https://library.tradingtechnologies.com/trade/chrt-ti-qstick.html
 
-    Calculation:
-        Default Inputs:
-            length=10
-        xMA is one of: sma (default), dema, ema, hma, rma
-        qstick = xMA(close - open, length)
-
     Args:
         open (pd.Series): Series of 'open's
         close (pd.Series): Series of 'close's
diff --git a/pandas_ta/trend/short_run.py b/pandas_ta/trend/short_run.py
index 2330d15..8d87788 100644
--- a/pandas_ta/trend/short_run.py
+++ b/pandas_ta/trend/short_run.py
@@ -21,18 +21,6 @@ def short_run(fast, slow, length=None, offset=None, **kwargs):
     It is part of the Converging and Diverging Conditional logic in:
         https://www.tradingview.com/script/Z2mq63fE-Trade-Archer-Moving-Averages-v1-4F/
 
-    Calculation:
-        Default Inputs:
-            length=2
-        INC = increasing
-        DEC = decreasing
-        BDEC = Both decreasing
-        PTOP = Potential Top
-
-        BDEC = DEC(fast, length) & DEC(slow, length)
-        PTOP = DEC(fast, length) & INC(slow, length)
-        SR = BDEC | PTOP
-
     Args:
         fast (pd.Series): Series of 'fast' values.
         slow (pd.Series): Series of 'slow' values.
diff --git a/pandas_ta/trend/trendflex.py b/pandas_ta/trend/trendflex.py
index 8ae0bb9..9aee235 100644
--- a/pandas_ta/trend/trendflex.py
+++ b/pandas_ta/trend/trendflex.py
@@ -25,9 +25,6 @@ def trendflex(close, length=None, smooth=None, alpha=None, offset=None, **kwargs
     Sources:
         https://www.prorealcode.com/prorealtime-indicators/reflex-and-trendflex-indicators-john-f-ehlers/
 
-    Calculation:
-        Refer to provided source or the code above.
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): It's period.  Default: 20
diff --git a/pandas_ta/trend/tsignals.py b/pandas_ta/trend/tsignals.py
index c8e2bb7..91075ef 100644
--- a/pandas_ta/trend/tsignals.py
+++ b/pandas_ta/trend/tsignals.py
@@ -20,14 +20,6 @@ def tsignals(trend, asbool=None, trend_reset=0, trade_offset=None, drift=None, o
 
     Source: Kevin Johnson
 
-    Calculation:
-        Default Inputs:
-            asbool=False, trend_reset=0, trade_offset=0, drift=1
-
-        trades = trends.diff().shift(trade_offset).fillna(0).astype(int)
-        entries = (trades > 0).astype(int)
-        exits = (trades < 0).abs().astype(int)
-
     Args:
         trend (pd.Series): Series of 'trend's. The trend can be either a boolean or
             integer series of '0's and '1's
diff --git a/pandas_ta/trend/ttm_trend.py b/pandas_ta/trend/ttm_trend.py
index c6cd8ac..d755d21 100644
--- a/pandas_ta/trend/ttm_trend.py
+++ b/pandas_ta/trend/ttm_trend.py
@@ -15,26 +15,17 @@ def ttm_trend(high, low, close, length=None, offset=None, **kwargs):
     Sources:
         https://www.prorealcode.com/prorealtime-indicators/ttm-trend-price/
 
-    Calculation:
-        Default Inputs:
-            length=6
-        averageprice = (((high[5]+low[5])/2)+((high[4]+low[4])/2)+((high[3]+low[3])/2)+((high[2]+low[2])/2)+((high[1]+low[1])/2)+((high[6]+low[6])/2)) / 6
-
-        if close > averageprice:
-            drawcandle(open,high,low,close) coloured(0,255,0)
-
-        if close < averageprice:
-            drawcandle(open,high,low,close) coloured(255,0,0)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
         close (pd.Series): Series of 'close's
         length (int): It's period. Default: 6
         offset (int): How many periods to offset the result. Default: 0
+
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
         fill_method (value, optional): Type of fill method
+
     Returns:
         pd.DataFrame: ttm_trend.
     """
diff --git a/pandas_ta/trend/vhf.py b/pandas_ta/trend/vhf.py
index e5b3b88..d461433 100644
--- a/pandas_ta/trend/vhf.py
+++ b/pandas_ta/trend/vhf.py
@@ -11,14 +11,6 @@ def vhf(close, length=None, drift=None, offset=None, **kwargs):
     Sources:
         https://www.incrediblecharts.com/indicators/vertical_horizontal_filter.php
 
-    Calculation:
-        Default Inputs:
-            length = 28
-        HCP = Highest Close Price in Period
-        LCP = Lowest Close Price in Period
-        Change = abs(Ct - Ct-1)
-        VHF = (HCP - LCP) / RollingSum[length] of Change
-
     Args:
         source (pd.Series): Series of prices (usually close).
         length (int): The period length. Default: 28
diff --git a/pandas_ta/trend/vortex.py b/pandas_ta/trend/vortex.py
index a1af6f1..929a817 100644
--- a/pandas_ta/trend/vortex.py
+++ b/pandas_ta/trend/vortex.py
@@ -12,20 +12,6 @@ def vortex(high, low, close, length=None, drift=None, offset=None, **kwargs):
     Sources:
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:vortex_indicator
 
-    Calculation:
-        Default Inputs:
-            length=14, drift=1
-        TR = True Range
-        SMA = Simple Moving Average
-        tr = TR(high, low, close)
-        tr_sum = tr.rolling(length).sum()
-
-        vmp = (high - low.shift(drift)).abs()
-        vmn = (low - high.shift(drift)).abs()
-
-        VIP = vmp.rolling(length).sum() / tr_sum
-        VIM = vmn.rolling(length).sum() / tr_sum
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/trend/xsignals.py b/pandas_ta/trend/xsignals.py
index 28d3dad..6962b1e 100644
--- a/pandas_ta/trend/xsignals.py
+++ b/pandas_ta/trend/xsignals.py
@@ -34,14 +34,6 @@ def xsignals(signal, xa, xb, above:bool=True, long:bool=True, asbool:bool=None,
 
     Source: Kevin Johnson
 
-    Calculation:
-        Default Inputs:
-            asbool=False, trend_reset=0, trade_offset=0, drift=1
-
-        trades = trends.diff().shift(trade_offset).fillna(0).astype(int)
-        entries = (trades > 0).astype(int)
-        exits = (trades < 0).abs().astype(int)
-
     Args:
         signal (pd.Series): The Signal to compare from. Commonly the 'close'.
         xa (pd.Series): The Series the Signal crosses above if 'above=True'.
diff --git a/pandas_ta/volatility/accbands.py b/pandas_ta/volatility/accbands.py
index de21502..28b7184 100644
--- a/pandas_ta/volatility/accbands.py
+++ b/pandas_ta/volatility/accbands.py
@@ -13,24 +13,6 @@ def accbands(high, low, close, length=None, c=None, drift=None, mamode=None, off
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/acceleration-bands-abands/
 
-    Calculation:
-        Default Inputs:
-            length=10, c=4
-        EMA = Exponential Moving Average
-        SMA = Simple Moving Average
-        HL_RATIO = c * (high - low) / (high + low)
-        LOW = low * (1 - HL_RATIO)
-        HIGH = high * (1 + HL_RATIO)
-
-        if 'ema':
-            LOWER = EMA(LOW, length)
-            MID = EMA(close, length)
-            UPPER = EMA(HIGH, length)
-        else:
-            LOWER = SMA(LOW, length)
-            MID = SMA(close, length)
-            UPPER = SMA(HIGH, length)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/atr.py b/pandas_ta/volatility/atr.py
index 3ab80a1..0c8f60f 100644
--- a/pandas_ta/volatility/atr.py
+++ b/pandas_ta/volatility/atr.py
@@ -14,28 +14,6 @@ def atr(high, low, close, length=None, mamode=None, talib=None, drift=None, offs
     Sources:
         https://www.tradingview.com/wiki/Average_True_Range_(ATR)
 
-    Calculation:
-        Default Inputs:
-            length=14, drift=1, percent=False
-        EMA = Exponential Moving Average
-        SMA = Simple Moving Average
-        WMA = Weighted Moving Average
-        RMA = WildeR's Moving Average
-        TR = True Range
-
-        tr = TR(high, low, close, drift)
-        if 'ema':
-            ATR = EMA(tr, length)
-        elif 'sma':
-            ATR = SMA(tr, length)
-        elif 'wma':
-            ATR = WMA(tr, length)
-        else:
-            ATR = RMA(tr, length)
-
-        if percent:
-            ATR *= 100 / close
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/bbands.py b/pandas_ta/volatility/bbands.py
index b068c9a..c06db7d 100644
--- a/pandas_ta/volatility/bbands.py
+++ b/pandas_ta/volatility/bbands.py
@@ -14,24 +14,6 @@ def bbands(close, length=None, std=None, ddof=0, mamode=None, talib=None, offset
     Sources:
         https://www.tradingview.com/wiki/Bollinger_Bands_(BB)
 
-    Calculation:
-        Default Inputs:
-            length=5, std=2, mamode="sma", ddof=0
-        EMA = Exponential Moving Average
-        SMA = Simple Moving Average
-        STDEV = Standard Deviation
-        stdev = STDEV(close, length, ddof)
-        if "ema":
-            MID = EMA(close, length)
-        else:
-            MID = SMA(close, length)
-
-        LOWER = MID - std * stdev
-        UPPER = MID + std * stdev
-
-        BANDWIDTH = 100 * (UPPER - LOWER) / MID
-        PERCENT = (close - LOWER) / (UPPER - LOWER)
-
     Args:
         close (pd.Series): Series of 'close's
         length (int): The short period. Default: 5
diff --git a/pandas_ta/volatility/donchian.py b/pandas_ta/volatility/donchian.py
index 761c9c3..9b098eb 100644
--- a/pandas_ta/volatility/donchian.py
+++ b/pandas_ta/volatility/donchian.py
@@ -12,13 +12,6 @@ def donchian(high, low, lower_length=None, upper_length=None, offset=None, **kwa
     Sources:
         https://www.tradingview.com/wiki/Donchian_Channels_(DC)
 
-    Calculation:
-        Default Inputs:
-            lower_length=upper_length=20
-        LOWER = low.rolling(lower_length).min()
-        UPPER = high.rolling(upper_length).max()
-        MID = 0.5 * (LOWER + UPPER)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/hwc.py b/pandas_ta/volatility/hwc.py
index 5bcc16e..9bd08f6 100644
--- a/pandas_ta/volatility/hwc.py
+++ b/pandas_ta/volatility/hwc.py
@@ -17,31 +17,21 @@ def hwc(close, na=None, nb=None, nc=None, nd=None, scalar=None, channel_eval=Non
     Sources:
         https://www.mql5.com/en/code/20857
 
-    Calculation:
-        HWMA[i] = F[i] + V[i] + 0.5 * A[i]
-        where..
-        F[i] = (1-na) * (F[i-1] + V[i-1] + 0.5 * A[i-1]) + na * Price[i]
-        V[i] = (1-nb) * (V[i-1] + A[i-1]) + nb * (F[i] - F[i-1])
-        A[i] = (1-nc) * A[i-1] + nc * (V[i] - V[i-1])
-
-        Top = HWMA + Multiplier * StDt
-        Bottom = HWMA - Multiplier * StDt
-        where..
-        StDt[i] = Sqrt(Var[i-1])
-        Var[i] = (1-d) * Var[i-1] + nD * (Price[i-1] - HWMA[i-1]) * (Price[i-1] - HWMA[i-1])
-
     Args:
-        na - parameter of the equation that describes a smoothed series (from 0 to 1)
-        nb - parameter of the equation to assess the trend (from 0 to 1)
-        nc - parameter of the equation to assess seasonality (from 0 to 1)
-        nd - parameter of the channel equation (from 0 to 1)
-        scaler - multiplier for the width of the channel calculated
-        channel_eval - boolean to return width and percentage price position against price
         close (pd.Series): Series of 'close's
+        na (float): Smoothed series (from 0 to 1). Default: 0.2
+        nb (float): Trend value (from 0 to 1). Default: 0.1
+        nc (float): Seasonality value (from 0 to 1). Default: 0.1
+        nd (float): Channel value (from 0 to 1). Default: 0.1
+        scaler (float): Width multiplier of the channel. Default: 1
+        channel_eval (bool): Return width and percentage price position against
+            price. Default: False
+        offset (int): How many periods to offset the result. Default: 0
 
     Kwargs:
         fillna (value, optional): pd.DataFrame.fillna(value)
         fill_method (value, optional): Type of fill method
+
     Returns:
         pd.DataFrame: HWM (Mid), HWU (Upper), HWL (Lower) columns.
     """
diff --git a/pandas_ta/volatility/kc.py b/pandas_ta/volatility/kc.py
index 4e5274a..176bb73 100644
--- a/pandas_ta/volatility/kc.py
+++ b/pandas_ta/volatility/kc.py
@@ -14,28 +14,6 @@ def kc(high, low, close, length=None, scalar=None, mamode=None, offset=None, **k
     Sources:
         https://www.tradingview.com/wiki/Keltner_Channels_(KC)
 
-    Calculation:
-        Default Inputs:
-            length=20, scalar=2, mamode=None, tr=True
-        TR = True Range
-        SMA = Simple Moving Average
-        EMA = Exponential Moving Average
-
-        if tr:
-            RANGE = TR(high, low, close)
-        else:
-            RANGE = high - low
-
-        if mamode == "ema":
-            BASIS = sma(close, length)
-            BAND = sma(RANGE, length)
-        elif mamode == "sma":
-            BASIS = sma(close, length)
-            BAND = sma(RANGE, length)
-
-        LOWER = BASIS - scalar * BAND
-        UPPER = BASIS + scalar * BAND
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/massi.py b/pandas_ta/volatility/massi.py
index 1aa24c1..da3c951 100644
--- a/pandas_ta/volatility/massi.py
+++ b/pandas_ta/volatility/massi.py
@@ -11,17 +11,6 @@ def massi(high, low, fast=None, slow=None, offset=None, **kwargs):
 
     Sources:
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:mass_index
-        mi = sum(ema(high - low, 9) / ema(ema(high - low, 9), 9), length)
-
-    Calculation:
-        Default Inputs:
-            fast: 9, slow: 25
-        EMA = Exponential Moving Average
-        hl = high - low
-        hl_ema1 = EMA(hl, fast)
-        hl_ema2 = EMA(hl_ema1, fast)
-        hl_ratio = hl_ema1 / hl_ema2
-        MASSI = SUM(hl_ratio, slow)
 
     Args:
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/volatility/natr.py b/pandas_ta/volatility/natr.py
index dca5890..46fc339 100644
--- a/pandas_ta/volatility/natr.py
+++ b/pandas_ta/volatility/natr.py
@@ -12,12 +12,6 @@ def natr(high, low, close, length=None, scalar=None, mamode=None, talib=None, dr
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/normalized-average-true-range-natr/
 
-    Calculation:
-        Default Inputs:
-            length=20
-        ATR = Average True Range
-        NATR = (100 / close) * ATR(high, low, close)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/pdist.py b/pandas_ta/volatility/pdist.py
index ae3ce9e..24c1d4c 100644
--- a/pandas_ta/volatility/pdist.py
+++ b/pandas_ta/volatility/pdist.py
@@ -10,12 +10,6 @@ def pdist(open_, high, low, close, drift=None, offset=None, **kwargs):
     Sources:
         https://www.prorealcode.com/prorealtime-indicators/pricedistance/
 
-    Calculation:
-        Default Inputs:
-            drift=1
-
-        PDIST = 2(high - low) - ABS(close - open) + ABS(open - close[drift])
-
     Args:
         open_ (pd.Series): Series of 'opens's
         high (pd.Series): Series of 'high's
diff --git a/pandas_ta/volatility/rvi.py b/pandas_ta/volatility/rvi.py
index abd7712..857f5c9 100644
--- a/pandas_ta/volatility/rvi.py
+++ b/pandas_ta/volatility/rvi.py
@@ -15,20 +15,6 @@ def rvi(close, high=None, low=None, length=None, scalar=None, refined=None, thir
     Sources:
         https://www.tradingview.com/wiki/Keltner_Channels_(KC)
 
-    Calculation:
-        Default Inputs:
-            length=14, scalar=100, refined=None, thirds=None
-        EMA = Exponential Moving Average
-        STDEV = Standard Deviation
-
-        UP = STDEV(src, length) IF src.diff() > 0 ELSE 0
-        DOWN = STDEV(src, length) IF src.diff() <= 0 ELSE 0
-
-        UPSUM = EMA(UP, length)
-        DOWNSUM = EMA(DOWN, length
-
-        RVI = scalar * (UPSUM / (UPSUM + DOWNSUM))
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/thermo.py b/pandas_ta/volatility/thermo.py
index 457671e..7a4b20d 100644
--- a/pandas_ta/volatility/thermo.py
+++ b/pandas_ta/volatility/thermo.py
@@ -13,22 +13,6 @@ def thermo(high, low, length=None, long=None, short=None, mamode=None, drift=Non
         https://www.motivewave.com/studies/elders_thermometer.htm
         https://www.tradingview.com/script/HqvTuEMW-Elder-s-Market-Thermometer-LazyBear/
 
-    Calculation:
-        Default Inputs:
-        length=20, drift=1, mamode=EMA, long=2, short=0.5
-        EMA = Exponential Moving Average
-
-        thermoL = (low.shift(drift) - low).abs()
-        thermoH = (high - high.shift(drift)).abs()
-
-        thermo = np.where(thermoH > thermoL, thermoH, thermoL)
-        thermo_ma = ema(thermo, length)
-
-        thermo_long = thermo < (thermo_ma * long)
-        thermo_short = thermo > (thermo_ma * short)
-        thermo_long = thermo_long.astype(int)
-        thermo_short = thermo_short.astype(int)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/true_range.py b/pandas_ta/volatility/true_range.py
index e73f905..9504d95 100644
--- a/pandas_ta/volatility/true_range.py
+++ b/pandas_ta/volatility/true_range.py
@@ -14,13 +14,6 @@ def true_range(high, low, close, talib=None, drift=None, offset=None, **kwargs):
     Sources:
         https://www.macroption.com/true-range/
 
-    Calculation:
-        Default Inputs:
-            drift=1
-        ABS = Absolute Value
-        prev_close = close.shift(drift)
-        TRUE_RANGE = ABS([high - low, high - prev_close, low - prev_close])
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volatility/ui.py b/pandas_ta/volatility/ui.py
index 428c4b0..d10f390 100644
--- a/pandas_ta/volatility/ui.py
+++ b/pandas_ta/volatility/ui.py
@@ -15,19 +15,6 @@ def ui(close, length=None, scalar=None, offset=None, **kwargs):
         https://en.wikipedia.org/wiki/Ulcer_index
         http://www.tangotools.com/ui/ui.htm
 
-    Calculation:
-        Default Inputs:
-            length=14, scalar=100
-        HC = Highest Close
-        SMA = Simple Moving Average
-
-        HCN = HC(close, length)
-        DOWNSIDE = scalar * (close - HCN) / HCN
-        if kwargs["everget"]:
-            UI = SQRT(SMA(DOWNSIDE^2, length) / length)
-        else:
-            UI = SQRT(SUM(DOWNSIDE^2, length) / length)
-
     Args:
         high (pd.Series): Series of 'high's
         close (pd.Series): Series of 'close's
diff --git a/pandas_ta/volume/__init__.py b/pandas_ta/volume/__init__.py
index 1539555..70a5030 100644
--- a/pandas_ta/volume/__init__.py
+++ b/pandas_ta/volume/__init__.py
@@ -13,5 +13,5 @@ from .pvi import pvi
 from .pvol import pvol
 from .pvr import pvr
 from .pvt import pvt
-from .tv_tsv import tv_tsv
 from .vp import vp
+from .wb_tsv import wb_tsv
diff --git a/pandas_ta/volume/ad.py b/pandas_ta/volume/ad.py
index 53f81f3..4911380 100644
--- a/pandas_ta/volume/ad.py
+++ b/pandas_ta/volume/ad.py
@@ -12,17 +12,6 @@ def ad(high, low, close, volume, open_=None, talib=None, offset=None, **kwargs):
     Sources:
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/accumulationdistribution-ad/
 
-    Calculation:
-        CUM = Cumulative Sum
-        if 'open':
-            AD = close - open
-        else:
-            AD = 2 * close - high - low
-
-        hl_range = high - low
-        AD = AD * volume / hl_range
-        AD = CUM(AD)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volume/adosc.py b/pandas_ta/volume/adosc.py
index b488b0e..fb93ba8 100644
--- a/pandas_ta/volume/adosc.py
+++ b/pandas_ta/volume/adosc.py
@@ -15,15 +15,6 @@ def adosc(high, low, close, volume, open_=None, fast=None, slow=None, talib=None
     Sources:
         https://www.investopedia.com/articles/active-trading/031914/understanding-chaikin-oscillator.asp
 
-    Calculation:
-        Default Inputs:
-            fast=12, slow=26
-        AD = Accum/Dist
-        ad = AD(high, low, close, open)
-        fast_ad = EMA(ad, fast)
-        slow_ad = EMA(ad, slow)
-        ADOSC = fast_ad - slow_ad
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volume/aobv.py b/pandas_ta/volume/aobv.py
index cdb8d22..3b8bc90 100644
--- a/pandas_ta/volume/aobv.py
+++ b/pandas_ta/volume/aobv.py
@@ -18,23 +18,6 @@ def aobv(close, volume, fast=None, slow=None, max_lookback=None, min_lookback=No
     Sources:
         https://www.tradingview.com/script/Co1ksara-Trade-Archer-On-balance-Volume-Moving-Averages-v1/
 
-    Calculation:
-        Default Inputs:
-            fast=4, slow=12, max_lookback=2, min_lookback=2, mamode="ema",
-            run_length=2
-        OBV = On Balance Volume
-        LR = Long Run Trend
-        SR = Short Run Trend
-
-        OBV_FMA = ma(OBV, mamode, fast)
-        OBV_SMA = ma(OBV, mamode, slow)
-
-        OBV_LR = LR(OBV_FMA, OBV_SMA, run_length)
-        OBV_SR = SR(OBV_FMA, OBV_SMA, run_length)
-
-        OBV_MAX = OBV.rolling(max_lookback).max()
-        OBV_MIN = OBV.rolling(min_lookback).min()
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/cmf.py b/pandas_ta/volume/cmf.py
index ff01bb8..c625c76 100644
--- a/pandas_ta/volume/cmf.py
+++ b/pandas_ta/volume/cmf.py
@@ -12,18 +12,6 @@ def cmf(high, low, close, volume, open_=None, length=None, offset=None, **kwargs
         https://www.tradingview.com/wiki/Chaikin_Money_Flow_(CMF)
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:chaikin_money_flow_cmf
 
-    Calculation:
-        Default Inputs:
-            length=20
-        if 'open':
-            ad = close - open
-        else:
-            ad = 2 * close - high - low
-
-        hl_range = high - low
-        ad = ad * volume / hl_range
-        CMF = SUM(ad, length) / SUM(volume, length)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volume/efi.py b/pandas_ta/volume/efi.py
index 923389d..394efc7 100644
--- a/pandas_ta/volume/efi.py
+++ b/pandas_ta/volume/efi.py
@@ -13,18 +13,6 @@ def efi(close, volume, length=None, mamode=None, drift=None, offset=None, **kwar
         https://www.tradingview.com/wiki/Elder%27s_Force_Index_(EFI)
         https://www.motivewave.com/studies/elders_force_index.htm
 
-    Calculation:
-        Default Inputs:
-            length=20, drift=1, mamode=None
-        EMA = Exponential Moving Average
-        SMA = Simple Moving Average
-
-        pv_diff = close.diff(drift) * volume
-        if mamode == 'sma':
-            EFI = SMA(pv_diff, length)
-        else:
-            EFI = EMA(pv_diff, length)
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/eom.py b/pandas_ta/volume/eom.py
index 3405b78..24ee77d 100644
--- a/pandas_ta/volume/eom.py
+++ b/pandas_ta/volume/eom.py
@@ -14,16 +14,6 @@ def eom(high, low, close, volume, length=None, divisor=None, drift=None, offset=
         https://www.motivewave.com/studies/ease_of_movement.htm
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:ease_of_movement_emv
 
-    Calculation:
-        Default Inputs:
-            length=14, divisor=100000000, drift=1
-        SMA = Simple Moving Average
-        hl_range = high - low
-        distance = 0.5 * (high - high.shift(drift) + low - low.shift(drift))
-        box_ratio = (volume / divisor) / hl_range
-        eom = distance / box_ratio
-        EOM = SMA(eom, length)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volume/kvo.py b/pandas_ta/volume/kvo.py
index 38fbb30..6b256d9 100644
--- a/pandas_ta/volume/kvo.py
+++ b/pandas_ta/volume/kvo.py
@@ -14,15 +14,6 @@ def kvo(high, low, close, volume, fast=None, slow=None, signal=None, mamode=None
         https://www.investopedia.com/terms/k/klingeroscillator.asp
         https://www.daytrading.com/klinger-volume-oscillator
 
-    Calculation:
-        Default Inputs:
-            fast=34, slow=55, signal=13, drift=1
-        EMA = Exponential Moving Average
-
-        SV = volume * signed_series(HLC3, 1)
-        KVO = EMA(SV, fast) - EMA(SV, slow)
-        Signal = EMA(KVO, signal)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volume/mfi.py b/pandas_ta/volume/mfi.py
index 8252e68..c6ea9e5 100644
--- a/pandas_ta/volume/mfi.py
+++ b/pandas_ta/volume/mfi.py
@@ -14,18 +14,6 @@ def mfi(high, low, close, volume, length=None, talib=None, drift=None, offset=No
     Sources:
         https://www.tradingview.com/wiki/Money_Flow_(MFI)
 
-    Calculation:
-        Default Inputs:
-            length=14, drift=1
-        tp = typical_price = hlc3 = (high + low + close) / 3
-        rmf = raw_money_flow = tp * volume
-
-        pmf = pos_money_flow = SUM(rmf, length) if tp.diff(drift) > 0 else 0
-        nmf = neg_money_flow = SUM(rmf, length) if tp.diff(drift) < 0 else 0
-
-        MFR = money_flow_ratio = pmf / nmf
-        MFI = money_flow_index = 100 * pmf / (pmf + nmf)
-
     Args:
         high (pd.Series): Series of 'high's
         low (pd.Series): Series of 'low's
diff --git a/pandas_ta/volume/nvi.py b/pandas_ta/volume/nvi.py
index 634d872..6c324d1 100644
--- a/pandas_ta/volume/nvi.py
+++ b/pandas_ta/volume/nvi.py
@@ -13,18 +13,6 @@ def nvi(close, volume, length=None, initial=None, offset=None, **kwargs):
         https://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:negative_volume_inde
         https://www.motivewave.com/studies/negative_volume_index.htm
 
-    Calculation:
-        Default Inputs:
-            length=1, initial=1000
-        ROC = Rate of Change
-
-        roc = ROC(close, length)
-        signed_volume = signed_series(volume, initial=1)
-        nvi = signed_volume[signed_volume < 0].abs() * roc_
-        nvi.fillna(0, inplace=True)
-        nvi.iloc[0]= initial
-        nvi = nvi.cumsum()
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/obv.py b/pandas_ta/volume/obv.py
index a079125..0a19f7e 100644
--- a/pandas_ta/volume/obv.py
+++ b/pandas_ta/volume/obv.py
@@ -14,10 +14,6 @@ def obv(close, volume, talib=None, offset=None, **kwargs):
         https://www.tradingtechnologies.com/help/x-study/technical-indicator-definitions/on-balance-volume-obv/
         https://www.motivewave.com/studies/on_balance_volume.htm
 
-    Calculation:
-        signed_volume = signed_series(close, initial=1) * volume
-        obv = signed_volume.cumsum()
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/pvi.py b/pandas_ta/volume/pvi.py
index 9174cd9..f2d4fce 100644
--- a/pandas_ta/volume/pvi.py
+++ b/pandas_ta/volume/pvi.py
@@ -13,18 +13,6 @@ def pvi(close, volume, length=None, initial=None, offset=None, **kwargs):
     Sources:
         https://www.investopedia.com/terms/p/pvi.asp
 
-    Calculation:
-        Default Inputs:
-            length=1, initial=1000
-        ROC = Rate of Change
-
-        roc = ROC(close, length)
-        signed_volume = signed_series(volume, initial=1)
-        pvi = signed_volume[signed_volume > 0].abs() * roc_
-        pvi.fillna(0, inplace=True)
-        pvi.iloc[0]= initial
-        pvi = pvi.cumsum()
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/pvol.py b/pandas_ta/volume/pvol.py
index 99ed75d..a3cbe05 100644
--- a/pandas_ta/volume/pvol.py
+++ b/pandas_ta/volume/pvol.py
@@ -7,12 +7,6 @@ def pvol(close, volume, offset=None, **kwargs):
 
     Returns a series of the product of price and volume.
 
-    Calculation:
-        if signed:
-            pvol = signed_series(close, 1) * close * volume
-        else:
-            pvol = close * volume
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/pvr.py b/pandas_ta/volume/pvr.py
index 19a13d9..7c9455e 100644
--- a/pandas_ta/volume/pvr.py
+++ b/pandas_ta/volume/pvr.py
@@ -16,12 +16,6 @@ def pvr(close, volume):
     Sources:
         https://www.fmlabs.com/reference/default.htm?url=PVrank.htm
 
-    Calculation:
-        return 1 if 'close change' >= 0 and 'volume change' >= 0
-        return 2 if 'close change' >= 0 and 'volume change' < 0
-        return 3 if 'close change' < 0 and 'volume change' >= 0
-        return 4 if 'close change' < 0 and 'volume change' < 0
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/pvt.py b/pandas_ta/volume/pvt.py
index 0b98cf1..10530ca 100644
--- a/pandas_ta/volume/pvt.py
+++ b/pandas_ta/volume/pvt.py
@@ -12,13 +12,6 @@ def pvt(close, volume, drift=None, offset=None, **kwargs):
     Sources:
         https://www.tradingview.com/wiki/Price_Volume_Trend_(PVT)
 
-    Calculation:
-        Default Inputs:
-            drift=1
-        ROC = Rate of Change
-        pv = ROC(close, drift) * volume
-        PVT = pv.cumsum()
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/tv_tsv.py b/pandas_ta/volume/tv_tsv.py
deleted file mode 100644
index 6521a52..0000000
--- a/pandas_ta/volume/tv_tsv.py
+++ /dev/null
@@ -1,96 +0,0 @@
-# -*- coding: utf-8 -*-
-import pandas as pd
-from pandas import DataFrame, Series
-from pandas_ta.utils import verify_series, signed_series
-import statistics
-import time
-from datetime import date, datetime, timedelta, timezone
-
-def tv_tsv(close=None, volume=None, length=None, signal=None, mamode=None, **kwargs):
-    """Indicator: Time Segmented Value (TSV)"""
-    # Validate Arguments
-    drift = kwargs.pop("drift", 1)
-    length = int(length) if length and length > 0 else 18
-    signal = int(signal) if signal and signal > 0 else 10
-    mamode = mamode if isinstance(mamode, str) else "sma"
-
-    # Trading View
-    # https://www.tradingview.com/script/6GR4ht9X-Time-Segmented-Volume/
-    # t = sum( close > close[1] ? volume*(close-close[1]) : close < close[1] ? volume*(close-close[1]) : 0,l)
-    # m = sma(t ,l_ma )
-
-    # Calculate Result
-    signed_volume = volume * ta.signed_series(close, 1)  # > 0
-    signed_volume[signed_volume < 0 ] = -signed_volume   # < 0
-    signed_volume.apply(ta.zero)                         # ~ 0
-    
-    cvd = (close.diff(drift) * signed_volume).fillna(0)
-        
-    tsv = (cvd.rolling(length).sum()).fillna(0)
-    signal = (ta.ma(mamode, tsv, length=signal)).fillna(0)
-    tsv_ratio = (tsv / signal).fillna(0)
-    
-    # Handle fills
-    if "fillna" in kwargs:
-        tsv.fillna(kwargs["fillna"], inplace=True)
-        signal.fillna(kwargs["fillna"], inplace=True)
-        tsv_ratio.fillna(kwargs["fillna"], inplace=True)
-    if "fill_method" in kwargs:
-        tsv.fillna(method=kwargs["fill_method"], inplace=True)
-        signal.fillna(method=kwargs["fill_method"], inplace=True)
-        tsv_ratio.fillna(method=kwargs["fill_method"], inplace=True)
-        
-
-    
-    # Name & Category
-    _props = f"_{length}_{signal}"
-    tv_tsv.name = f"TV_TSV{_props}"
-    tv_tsv.category = "volume"
-
-    #tsv.name = "tsv"
-    
-
-    df = pd.DataFrame({
-        "close": close, "volume": volume,
-        "signed_volume": signed_volume, "cvd": cvd,
-        "tsv": tsv, "signal": signal, "tsv_ratio": tsv_ratio
-    })
-
-    return df
-
-
-
-tv_tsv.__doc__ = \
-"""Time Segmented Value (TSV)
-
-TSV is a proprietary technical indicator developed by Worden Brothers Inc., classified as an oscillator. 
-It is calculated by comparing various time segments of both price and volume. 
-TSV essentially measures the amount of money flowing in or out of a particular stock. 
-The baseline represents the zero line.
-
-TSV is a leading indicator because its movement is based on both the stock's price fluctuation and volume. 
-Ideal entry and exit points are commonly found as the stock moves across the baseline level. 
-This indicator is similar to on-balance volume (OBV) because it measures the amount of money flowing in or out of a particular stock.
-
-Sources:
-    https://www.investopedia.com/terms/t/tsv.asp
-
-Calculation:
-    Default Inputs:
-        length=18, signal=10
-
-
-Args:
-    close (pd.Series): Series of 'close's
-    volume (pd.Series): Series of 'volume's
-    length (int): It's period. Default: 18
-    signal (int): It's avg period. Default: 10
-    mamode (str): See ```help(ta.ma)```. Default: 'sma'
-
-Kwargs:
-    fillna (value, optional): pd.DataFrame.fillna(value)
-    fill_method (value, optional): Type of fill method
-
-Returns:
-    pd.DataFrame: tsv, signal, tsv_ratio
-"""
diff --git a/pandas_ta/volume/vp.py b/pandas_ta/volume/vp.py
index c1d8a42..6dd88ed 100644
--- a/pandas_ta/volume/vp.py
+++ b/pandas_ta/volume/vp.py
@@ -17,20 +17,6 @@ def vp(close, volume, width=None, **kwargs):
         http://www.ranchodinero.com/volume-tpo-essentials/
         https://www.tradingtechnologies.com/blog/2013/05/15/volume-at-price/
 
-    Calculation:
-        Default Inputs:
-            width=10
-
-        vp = pd.concat([close, pos_volume, neg_volume], axis=1)
-        if sort_close:
-            vp_ranges = cut(vp[close_col], width)
-            result = ({range_left, mean_close, range_right, pos_volume, neg_volume} foreach range in vp_ranges
-        else:
-            vp_ranges = np.array_split(vp, width)
-            result = ({low_close, mean_close, high_close, pos_volume, neg_volume} foreach range in vp_ranges
-        vpdf = pd.DataFrame(result)
-        vpdf['total_volume'] = vpdf['pos_volume'] + vpdf['neg_volume']
-
     Args:
         close (pd.Series): Series of 'close's
         volume (pd.Series): Series of 'volume's
diff --git a/pandas_ta/volume/wb_tsv.py b/pandas_ta/volume/wb_tsv.py
new file mode 100644
index 0000000..bae6726
--- /dev/null
+++ b/pandas_ta/volume/wb_tsv.py
@@ -0,0 +1,84 @@
+# -*- coding: utf-8 -*-
+from pandas import DataFrame, Series
+from pandas_ta.overlap import ma
+from pandas_ta.utils import get_drift, get_offset, verify_series, signed_series, zero
+
+
+def wb_tsv(close=None, volume=None, length=None, signal=None, mamode=None, drift=None, offset=None, **kwargs):
+    """Time Segmented Value (TSV)
+
+    TSV is a proprietary technical indicator developed by Worden Brothers Inc.,
+    classified as an oscillator. It compares various time segments of both price
+    and volume. It measures the amount money flowing at various time segments
+    for price and time; similar to On Balance Volume. The zero line is called
+    the baseline. Entry and exit points are commonly determined when crossing
+    the baseline.
+
+    Sources:
+        https://www.tradingview.com/script/6GR4ht9X-Time-Segmented-Volume/
+        https://help.tc2000.com/m/69404/l/747088-time-segmented-volume
+        https://usethinkscript.com/threads/time-segmented-volume-for-thinkorswim.519/
+
+    Args:
+        close (pd.Series): Series of 'close's
+        volume (pd.Series): Series of 'volume's
+        length (int): It's period. Default: 18
+        signal (int): It's avg period. Default: 10
+        mamode (str): See ```help(ta.ma)```. Default: 'sma'
+        drift (int): The difference period. Default: 1
+        offset (int): How many periods to offset the result. Default: 0
+
+    Kwargs:
+        fillna (value, optional): pd.DataFrame.fillna(value)
+        fill_method (value, optional): Type of fill method
+
+    Returns:
+        pd.DataFrame: tsv, signal, ratio
+    """
+    # Validate Arguments
+    length = int(length) if length and length > 0 else 18
+    signal = int(signal) if signal and signal > 0 else 10
+    mamode = mamode if isinstance(mamode, str) else "sma"
+    drift = get_drift(drift)
+    offset = get_offset(offset)
+
+    # Calculate Result
+    signed_volume = volume * signed_series(close, 1)     # > 0
+    signed_volume[signed_volume < 0 ] = -signed_volume   # < 0
+    signed_volume.apply(zero)                            # ~ 0
+    cvd = signed_volume * close.diff(drift)
+
+    tsv = cvd.rolling(length).sum()
+    signal_ = ma(mamode, tsv, length=signal)
+    ratio = tsv / signal_
+
+    # Offset
+    if offset != 0:
+        tsv = tsv.shift(offset)
+        signal_ = signal.shift(offset)
+        ratio = ratio.shift(offset)
+
+    # Handle fills
+    if "fillna" in kwargs:
+        tsv.fillna(kwargs["fillna"], inplace=True)
+        signal_.fillna(kwargs["fillna"], inplace=True)
+        ratio.fillna(kwargs["fillna"], inplace=True)
+    if "fill_method" in kwargs:
+        tsv.fillna(method=kwargs["fill_method"], inplace=True)
+        signal_.fillna(method=kwargs["fill_method"], inplace=True)
+        ratio.fillna(method=kwargs["fill_method"], inplace=True)
+
+    # Name and Categorize
+    _props = f"_{length}_{signal}"
+    tsv.name = f"TSV{_props}"
+    signal_.name = f"TSVs{_props}"
+    ratio.name = f"TSVr{_props}"
+    tsv.category = signal_.category = ratio.category = "volume"
+
+    # Prepare DataFrame to return
+    data = {tsv.name: tsv, signal_.name: signal_, ratio.name: ratio}
+    df = DataFrame(data)
+    df.name = f"TSV{_props}"
+    df.category = tsv.category
+
+    return df
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 6fbcc2d..e4c9197 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "3", "39b")),
+    version=".".join(("0", "3", "40b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",
diff --git a/tests/test_ext_indicator_volume.py b/tests/test_ext_indicator_volume.py
index 1ca417e..70da6f2 100644
--- a/tests/test_ext_indicator_volume.py
+++ b/tests/test_ext_indicator_volume.py
@@ -1,3 +1,4 @@
+from unittest.case import skip
 from .config import sample_data
 from .context import pandas_ta
 
@@ -100,7 +101,13 @@ class TestVolumeExtension(TestCase):
         self.assertIsInstance(self.data, DataFrame)
         self.assertEqual(self.data.columns[-1], "PVT")
 
+    @skip("\nVP does not return a Time Series")
     def test_vp_ext(self):
         result = self.data.ta.vp()
         self.assertIsInstance(result, DataFrame)
         self.assertEqual(result.name, "VP_10")
+
+    def test_wb_tsv_ext(self):
+        self.data.ta.wb_tsv(append=True)
+        self.assertIsInstance(self.data, DataFrame)
+        self.assertEqual(list(self.data.columns[-3:]), ["TSV_18_10", "TSVs_18_10", "TSVr_18_10"])
diff --git a/tests/test_indicator_momentum.py b/tests/test_indicator_momentum.py
index aa467e5..b7f691d 100644
--- a/tests/test_indicator_momentum.py
+++ b/tests/test_indicator_momentum.py
@@ -446,7 +446,7 @@ class TestMomentum(TestCase):
 
     def test_stoch(self):
         # TV Correlation
-        result = pandas_ta.stoch(self.high, self.low, self.close)
+        result = pandas_ta.stoch(self.high, self.low, self.close, talib=False)
         self.assertIsInstance(result, DataFrame)
         self.assertEqual(result.name, "STOCH_14_3_3")
 
@@ -467,6 +467,10 @@ class TestMomentum(TestCase):
             except Exception as ex:
                 error_analysis(result.iloc[:, 1], CORRELATION, ex, newline=False)
 
+        result = pandas_ta.stoch(self.high, self.low, self.close)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "STOCH_14_3_3")
+
     def test_stochf(self):
         # TV Correlation
         result = pandas_ta.stochf(self.high, self.low, self.close, talib=False)
diff --git a/tests/test_indicator_volume.py b/tests/test_indicator_volume.py
index dae1f54..518f7c1 100644
--- a/tests/test_indicator_volume.py
+++ b/tests/test_indicator_volume.py
@@ -174,3 +174,8 @@ class TestVolume(TestCase):
         result = pandas_ta.vp(self.close, self.volume_)
         self.assertIsInstance(result, DataFrame)
         self.assertEqual(result.name, "VP_10")
+
+    def test_wb_tsv(self):
+        result = pandas_ta.wb_tsv(self.close, self.volume_)
+        self.assertIsInstance(result, DataFrame)
+        self.assertEqual(result.name, "TSV_18_10")

From 3aac80c2c12ded12d0afc47fd46cb7f7f43517fc Mon Sep 17 00:00:00 2001
From: Kevin Johnson <appliedmathkj@gmail.com>
Date: Fri, 17 Dec 2021 11:18:00 -0800
Subject: [PATCH 6/6] BUG #454 initial period fix

---
 README.md                | 2 +-
 pandas_ta/overlap/jma.py | 2 +-
 setup.py                 | 2 +-
 3 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.md b/README.md
index c948fcb..7a98ed5 100644
--- a/README.md
+++ b/README.md
@@ -118,7 +118,7 @@ $ pip install pandas_ta
 
 Latest Version
 --------------
-Best choice! Version: *0.3.40b*
+Best choice! Version: *0.3.41b*
 * Includes all fixes and updates between **pypi** and what is covered in this README.
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta
diff --git a/pandas_ta/overlap/jma.py b/pandas_ta/overlap/jma.py
index 680e1fa..ebd851b 100644
--- a/pandas_ta/overlap/jma.py
+++ b/pandas_ta/overlap/jma.py
@@ -95,8 +95,8 @@ def jma(close, length=None, phase=None, offset=None, **kwargs):
         jma[i] = jma[i-1] + det1
 
     # Remove initial lookback data and convert to pandas frame
-    jma[0:_length - 1] = npNaN
     jma = Series(jma, index=close.index)
+    jma.iloc[0:_length - 1] = npNaN
 
     # Offset
     if offset != 0:
diff --git a/setup.py b/setup.py
index e4c9197..0789f02 100644
--- a/setup.py
+++ b/setup.py
@@ -19,7 +19,7 @@ setup(
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "3", "40b")),
+    version=".".join(("0", "3", "41b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",