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Technical Analysis with Pandas (pandas_ta)

  • Below contains examples of simple charts that can be made from pandas_ta indicators
  • Examples below are for educational purposes only
  • NOTE: The watchlist module is independent of Pandas TA. To easily use it, copy it from your local pandas_ta installation directory into your project directory.
In [1]:
%matplotlib inline
import datetime as dt
import random as rnd

from tqdm import tqdm
import numpy as np
import pandas as pd
import mplfinance as mpf

from alphaVantageAPI.alphavantage import AlphaVantage
import pandas_ta as ta

from watchlist import colors # Is this failing? If so, copy it locally. See above.

print(f"\nPandas TA v{ta.version}\nTo install the Latest Version:\n$ pip install -U git+https://github.com/twopirllc/pandas-ta\n")

%pylab inline
Pandas TA v0.3.32b0
To install the Latest Version:
$ pip install -U git+https://github.com/twopirllc/pandas-ta

Populating the interactive namespace from numpy and matplotlib

List of Indicators (post an issue if the indicator doc needs updating)

In [2]:
e = pd.DataFrame()
e.ta.indicators()
Pandas TA - Technical Analysis Indicators - v0.3.32b0

Indicators and Utilities [149]:
    aberration, above, above_value, accbands, ad, adosc, adx, alligator, alma, amat, ao, aobv, apo, aroon, atr, bbands, below, below_value, bias, bop, brar, cci, cdl_pattern, cdl_z, cfo, cg, chop, cksp, cmf, cmo, coppock, cross, cross_value, cti, decay, decreasing, dema, dm, donchian, dpo, ebsw, efi, ema, entropy, eom, er, eri, fisher, fwma, ha, hilo, hl2, hlc3, hma, hwc, hwma, ichimoku, increasing, inertia, jma, kama, kc, kdj, kst, kurtosis, kvo, linreg, log_return, long_run, macd, mad, massi, mcgd, median, mfi, midpoint, midprice, mom, natr, nvi, obv, ohlc4, pdist, percent_return, pgo, ppo, psar, psl, pvi, pvo, pvol, pvr, pvt, pwma, qqe, qstick, quantile, reflex, rma, roc, rsi, rsx, rvgi, rvi, short_run, sinwma, skew, slope, sma, smi, smma, squeeze, squeeze_pro, ssf, ssf3, stc, stdev, stoch, stochf, stochrsi, supertrend, swma, t3, td_seq, tema, thermo, tos_stdevall, trendflex, trima, trix, true_range, tsi, tsignals, ttm_trend, ui, uo, variance, vhf, vidya, vortex, vwap, vwma, wb_tsv, wcp, willr, wma, xsignals, zlma, zscore

Candle Patterns [62]:
    2crows, 3blackcrows, 3inside, 3linestrike, 3outside, 3starsinsouth, 3whitesoldiers, abandonedbaby, advanceblock, belthold, breakaway, closingmarubozu, concealbabyswall, counterattack, darkcloudcover, doji, dojistar, dragonflydoji, engulfing, eveningdojistar, eveningstar, gapsidesidewhite, gravestonedoji, hammer, hangingman, harami, haramicross, highwave, hikkake, hikkakemod, homingpigeon, identical3crows, inneck, inside, invertedhammer, kicking, kickingbylength, ladderbottom, longleggeddoji, longline, marubozu, matchinglow, mathold, morningdojistar, morningstar, onneck, piercing, rickshawman, risefall3methods, separatinglines, shootingstar, shortline, spinningtop, stalledpattern, sticksandwich, takuri, tasukigap, thrusting, tristar, unique3river, upsidegap2crows, xsidegap3methods

Total Candles, Indicators and Utilities: 211

Individual Indicator help

In [3]:
help(ta.ema)
Help on function ema in module pandas_ta.overlap.ema:

ema(close, length=None, talib=None, presma=None, offset=None, **kwargs)
    Exponential Moving Average (EMA)
    
    The Exponential Moving Average is more responsive moving average compared to the
    Simple Moving Average (SMA).  The weights are determined by alpha which is
    proportional to it's length.  There are several different methods of calculating
    EMA.  One method uses just the standard definition of EMA and another uses the
    SMA to generate the initial value for the rest of the calculation.
    
    Sources:
        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
    
    Args:
        close (pd.Series): Series of 'close's
        length (int): It's period. Default: 10
        talib (bool): If TA Lib is installed and talib=True, it returns the
            TA Lib values. Default: True
        presma (bool, optional): If True, uses SMA for initial value like TA Lib.
            Default: True
        offset (int): How many periods to offset the result. Default: 0
    
    Kwargs:
        adjust (bool, optional): Default: False
        fillna (value, optional): pd.DataFrame.fillna(value)
        fill_method (value, optional): Type of fill method
    
    Returns:
        pd.Series: New feature generated.
In [4]:
# Function to format Millions
def format_millions(x, pos):
    "The two args are the value and tick position"
    return "%1.1fM" % (x * 1e-6)
In [5]:
def ctitle(indicator_name, ticker="SPY", length=100):
    return f"{ticker}: {indicator_name} from {recent_startdate} to {recent_enddate} ({length})"

# # All Data: 0, Last Four Years: 0.25, Last Two Years: 0.5, This Year: 1, Last Half Year: 2, Last Quarter: 3
# yearly_divisor = 1
# recent = int(ta.RATE["TRADING_DAYS_PER_YEAR"] / yearly_divisor) if yearly_divisor > 0 else df.shape[0]
# print(recent)
def recent_bars(df, tf: str = "1y"):
    # All Data: 0, Last Four Years: 0.25, Last Two Years: 0.5, This Year: 1, Last Half Year: 2, Last Quarter: 4
    yearly_divisor = {"all": 0, "10y": 0.1, "5y": 0.2, "4y": 0.25, "3y": 1./3, "2y": 0.5, "1y": 1, "6mo": 2, "3mo": 4}
    yd = yearly_divisor[tf] if tf in yearly_divisor.keys() else 0
    return int(ta.RATE["TRADING_DAYS_PER_YEAR"] / yd) if yd > 0 else df.shape[0]

def ta_ylim(series: pd.Series, percent: float = 0.1):
    smin, smax = series.min(), series.max()
    if isinstance(percent, float) and 0 <= float(percent) <= 1:
        y_min = (1 + percent) * smin if smin < 0 else (1 - percent) * smin
        y_max = (1 - percent) * smax if smax < 0 else (1 + percent) * smax
        return (y_min, y_max)
    return (smin, smax)

price_size = (16, 8)
ind_size = (16, 3.25)

Load Daily Ticker Data using yfinance and clean it

In [6]:
# help(e.ta.ticker)
In [7]:
# Recent Data
ticker = "BTC-USD"
ticker = "SPY"
df = e.ta.ticker(ticker, kind="info", lc_cols=True)
recent_startdate = df.tail(recent_bars(df)).index[0]
recent_enddate = df.tail(recent_bars(df)).index[-1]
print(f"{df.name}{df.tail(recent_bars(df)).shape} from {recent_startdate} to {recent_enddate}")
df.tail(recent_bars(df)).head()
Out [7]:
====  Company Information  =====================================================
SPDR S&P 500 ETF Trust(SPDR S&P 500) [SPY]

====  Market Information   =====================================================
Market | Exchange | Symbol | Category               US | PCX | SPY | Large Blend

NAV | Yield                                                     477.53 | 1.3000%



====  Price Information    =====================================================
Open High Low | Close                    472.1900  472.1900  469.1800 | 471.1600
HL2 | HLC3 | OHLC4 | C - OHLC4             471.0300, 471.0733, 471.3525, -0.1925
Change (%)                                                      0.1400 (0.0297%)
Bid | Ask | Spread                        470.97 x 800 | 470.95 x 1000 | -0.0200
Volume | Market | Avg Vol (10Day)       
                               21,328,326 | 21,328,326 | 73,562,134 (80,200,220)

52Wk Range (% from 52Wk Low)               368.27 - 479.98 : 111.7100 (27.9387%)
SMA 50 | SMA 200                                             466.9138 | 440.3433
Avg. Return 3Yr | 5Yr                                        24.8800% | 17.5900%

====  Dividends / Splits   =====================================================
Trailing Annual Dividend Rate | Yield                            5.563 | 1.1811%


Stock Splits (Last 5 of 117):
Date   2021-12-17  2021-09-17  2021-06-18  2021-03-19  2020-12-18
Ratio       1.633       1.428       1.376       1.278        1.58
SPY(252, 7) from 2021-01-15 00:00:00 to 2022-01-13 00:00:00
open high low close volume dividends stock splits
date
2021-01-15 371.764445 372.613118 368.784182 370.757874 107160000 0.0 0
2021-01-19 373.363148 374.241455 371.794067 373.669067 51233300 0.0 0
2021-01-20 376.096689 379.728304 375.682231 378.840149 61836100 0.0 0
2021-01-21 379.432228 379.886198 378.208549 379.185516 47840100 0.0 0
2021-01-22 377.221697 379.076971 376.817087 377.843414 52860500 0.0 0

Aliases

In [8]:
opendf = df["open"]
highdf = df["high"]
lowdf = df["low"]
closedf = df["close"]
volumedf = df["volume"]

DataFrame constants: When you need some simple lines for charting

In [9]:
# help(df.ta.constants) # for more info
chart_lines = np.append(np.arange(-5, 6, 1), np.arange(-100, 110, 10))
df.ta.constants(True, chart_lines) # Adding the constants for the charts
df.ta.constants(False, np.array([-60, -40, 40, 60])) # Removing some constants from the DataFrame
print(f"Columns: {', '.join(list(df.columns))}")
Columns: open, high, low, close, volume, dividends, stock splits, -5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, -100, -90, -80, -70, -50, -30, -20, -10, 10, 20, 30, 50, 70, 80, 90, 100

Example Charting Class utilizing mplfinance panels

In [10]:
class Chart(object):
    def __init__(self, df: pd.DataFrame = None, strategy: ta.Strategy = ta.CommonStrategy, *args, **kwargs):
        self.verbose = kwargs.pop("verbose", False)

        if isinstance(df, pd.DataFrame) and df.ta.datetime_ordered:
            self.df = df
            if self.df.name is not None and self.df.name != "":
                df_name = str(self.df.name)
            else:
                df_name = "DataFrame"
            if self.verbose: print(f"[i] Loaded {df_name}{self.df.shape}")
        else:
            print(f"[X] Oops! Missing 'ohlcv' data or index is not datetime ordered.\n")
            return None

        self._validate_mpf_kwargs(**kwargs)
        self._validate_chart_kwargs(**kwargs)
        self._validate_ta_strategy(strategy)

        # Build TA and Plot
        self.df.ta.strategy(self.strategy, verbose=self.verbose)
        self._plot(**kwargs)

    def _validate_ta_strategy(self, strategy):
        if strategy is not None or isinstance(strategy, ta.Strategy):
            self.strategy = strategy
        elif len(self.strategy_ta) > 0:
            print(f"[+] Strategy: {self.strategy_name}")
        else:
            self.strategy = ta.CommonStrategy        

    def _validate_chart_kwargs(self, **kwargs):
        """Chart Settings"""
        self.config = {}
        self.config["last"] = kwargs.pop("last", recent_bars(self.df))
        self.config["rpad"] = kwargs.pop("rpad", 10)
        self.config["title"] = kwargs.pop("title", "Asset")
        self.config["volume"] = kwargs.pop("volume", True)

    def _validate_mpf_kwargs(self, **kwargs):
        # mpf global chart settings
        default_chart = mpf.available_styles()[-1]
        default_mpf_width = {
            'candle_linewidth': 0.6,
            'candle_width': 0.525,
            'volume_width': 0.525
        }
        mpfchart = {}

        mpf_style = kwargs.pop("style", "")
        if mpf_style == "" or mpf_style.lower() == "random":
            mpf_styles = mpf.available_styles()
            mpfchart["style"] = mpf_styles[rnd.randrange(len(mpf_styles))]
        elif mpf_style.lower() in mpf.available_styles():
            mpfchart["style"] = mpf_style

        mpfchart["figsize"] = kwargs.pop("figsize", (12, 10))
        mpfchart["non_trading"] = kwargs.pop("nontrading", False)
        mpfchart["rc"] = kwargs.pop("rc", {'figure.facecolor': '#EDEDED'})
        mpfchart["plot_ratios"] = kwargs.pop("plot_ratios", (12, 1.7))
        mpfchart["scale_padding"] = kwargs.pop("scale_padding", {'left': 1, 'top': 4, 'right': 1, 'bottom': 1})
        mpfchart["tight_layout"] = kwargs.pop("tight_layout", True)
        mpfchart["type"] = kwargs.pop("type", "candle")
        mpfchart["width_config"] = kwargs.pop("width_config", default_mpf_width)
        mpfchart["xrotation"] = kwargs.pop("xrotation", 15)
        
        self.mpfchart = mpfchart

    def _attribution(self):
        print(f"\nPandas v: {pd.__version__} [pip install pandas] https://github.com/pandas-dev/pandas")
        print(f"Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI")
        print(f"Technical Analysis with Pandas TA v: {ta.version} [pip install pandas_ta] https://github.com/twopirllc/pandas-ta")
        print(f"Charts by Matplotlib Finance v: {mpf.__version__} [pip install mplfinance] https://github.com/matplotlib/mplfinance\n")

    def _right_pad_df(self, rpad: int, delta_unit: str = "D", range_freq: str = "B"):
        if rpad > 0:
            dfpad = self.df[-rpad:].copy()
            dfpad.iloc[:,:] = np.NaN

            df_frequency = self.df.index.value_counts().mode()[0] # Most common frequency
            freq_delta = pd.Timedelta(df_frequency, unit=delta_unit)
            new_dr = pd.date_range(start=self.df.index[-1] + freq_delta, periods=rpad, freq=range_freq)
            dfpad.index = new_dr # Update the padded index with new dates
            self.df = self.df.append(dfpad)
        
            
    def _plot(self, **kwargs):
        if not isinstance(self.mpfchart["plot_ratios"], tuple):
            print(f"[X] plot_ratios must be a tuple")
            return

        # Override Chart Title Option
        chart_title = self.config["title"]
        if "title" in kwargs and isinstance(kwargs["title"], str):
            chart_title = kwargs.pop("title")

        # Override Right Bar Padding Option
        rpad = self.config["rpad"]
        if "rpad" in kwargs and kwargs["rpad"] > 0:
            rpad = int(kwargs["rpad"])

        def cpanel():
            return len(self.mpfchart["plot_ratios"])

        # Last Second Default TA Indicators
        linreg = kwargs.pop("linreg", False)
        linreg_name = self.df.ta.linreg(append=True).name if linreg else ""

        midpoint = kwargs.pop("midpoint", False)
        midpoint_name = self.df.ta.midpoint(append=True).name if midpoint else ""

        ohlc4 = kwargs.pop("ohlc4", False)
        ohlc4_name = self.df.ta.ohlc4(append=True).name if ohlc4 else ""

        clr = kwargs.pop("clr", False)
        clr_name = self.df.ta.log_return(cumulative=True, append=True).name if clr else ""

        rsi = kwargs.pop("rsi", False)
        rsi_length = kwargs.pop("rsi_length", None)
        if isinstance(rsi_length, int) and rsi_length > 1:
            rsi_name = self.df.ta.rsi(length=rsi_length, append=True).name
        elif rsi:
            rsi_name = self.df.ta.rsi(append=True).name
        else: rsi_name = ""
            
        zscore = kwargs.pop("zscore", False)
        zscore_length = kwargs.pop("zscore_length", None)
        if isinstance(zscore_length, int) and zscore_length > 1:
            zs_name = self.df.ta.zscore(length=zscore_length, append=True).name
        elif zscore:
            zs_name = self.df.ta.zscore(append=True).name
        else: zs_name = ""

        macd = kwargs.pop("macd", False)
        macd_name = ""
        if macd:
            macds = self.df.ta.macd(append=True)
            macd_name = macds.name

        squeeze = kwargs.pop("squeeze", False)
        lazybear = kwargs.pop("lazybear", False)
        squeeze_name = ""
        if squeeze:
            squeezes = self.df.ta.squeeze(lazybear=lazybear, detailed=True, append=True)
            squeeze_name = squeezes.name

        ama = kwargs.pop("archermas", False)
        ama_name = ""
        if ama:
            amas = self.df.ta.amat(append=True)
            ama_name = amas.name

        aobv = kwargs.pop("archerobv", False)
        aobv_name = ""
        if aobv:
            aobvs = self.df.ta.aobv(append=True)
            aobv_name = aobvs.name

        # Pad and trim Chart
        self._right_pad_df(rpad)
        mpfdf = self.df.tail(self.config["last"]).copy()
        mpfdf_columns = list(self.df.columns)
        
        tsig = kwargs.pop("tsignals", False)
        if tsig:
            # Long Trend requires Series Comparison (<=. <, = >, >=)
            # or Trade Logic that yields trends in binary.
            default_long = mpfdf["SMA_10"] > mpfdf["SMA_20"]
            long_trend = kwargs.pop("long_trend", default_long)
            if not isinstance(long_trend, pd.Series):
                raise(f"[X] Must be a Series that has boolean values or values of 0s and 1s")
            mpfdf.ta.percent_return(append=True)
            mpfdf.ta.tsignals(long_trend, append=True)
            buys = np.where(mpfdf.TS_Entries > 0, 1, np.nan)
            sells = np.where(mpfdf.TS_Exits > 0, 1, np.nan)
            mpfdf["ACTRET_1"] = mpfdf.TS_Trends * mpfdf.PCTRET_1

        # BEGIN: Custom TA Plots and Panels
        # Modify the area below 
        taplots = [] # Holds all the additional plots

        # Panel 0: Price Overlay
        if linreg_name in mpfdf_columns:
            taplots += [mpf.make_addplot(mpfdf[linreg_name], type=kwargs.pop("linreg_type", "line"), color=kwargs.pop("linreg_color", "black"), linestyle="-.", width=1.2, panel=0)]

        if midpoint_name in mpfdf_columns:
            taplots += [mpf.make_addplot(mpfdf[midpoint_name], type=kwargs.pop("midpoint_type", "scatter"), color=kwargs.pop("midpoint_color", "fuchsia"), width=0.4, panel=0)]

        if ohlc4_name in mpfdf_columns:
            taplots += [mpf.make_addplot(mpfdf[ohlc4_name], ylabel=ohlc4_name, type=kwargs.pop("ohlc4_type", "scatter"), color=kwargs.pop("ohlc4_color", "blue"), alpha=0.85, width=0.4, panel=0)]
    
        if self.strategy.name == ta.CommonStrategy.name:
            total_sma = 0 # Check if all the overlap indicators exists before adding plots
            for c in ["SMA_10", "SMA_20", "SMA_50", "SMA_200"]:
                if c in mpfdf_columns: total_sma += 1
                else: print(f"[X] Indicator: {c} missing!")
            if total_sma == 4:
                ta_smas = [
                    mpf.make_addplot(mpfdf["SMA_10"], color="green", width=1.5, panel=0),
                    mpf.make_addplot(mpfdf["SMA_20"], color="orange", width=2, panel=0),
                    mpf.make_addplot(mpfdf["SMA_50"], color="red", width=2, panel=0),
                    mpf.make_addplot(mpfdf["SMA_200"], color="maroon", width=3, panel=0),
                ]
                taplots += ta_smas

        if tsig:
            taplots += [
                mpf.make_addplot(0.985 * mpfdf.close * buys, type="scatter", marker="^", markersize=26, color="blue", panel=0),
                mpf.make_addplot(1.015 * mpfdf.close * sells, type="scatter", marker="v", markersize=26, color="fuchsia", panel=0),
            ]
                
        if len(ama_name):
            amat_sr_ = mpfdf[amas.columns[-1]][mpfdf[amas.columns[-1]] > 0]
            amat_sr = amat_sr_.index.to_list()
        else:
            amat_sr = None

        # Panel 1: If volume=True, the add the VOL MA. Since we know there is only one, we immediately pop it.
        if self.config["volume"]:
            volma = [x for x in list(self.df.columns) if x.startswith("VOL_")].pop()
            max_vol = mpfdf["volume"].max()
             # Volume axis
            ta_volume = [mpf.make_addplot(mpfdf[volma], color="black", width=1.2, panel=1, ylim=(-.2 * max_vol, 1.5 * max_vol))]
            taplots += ta_volume

        # Panels 2 - 9
        common_plot_ratio = (3,)

        if len(aobv_name):
            _p = kwargs.pop("aobv_percenty", 0.2)
            aobv_ylim = ta_ylim(mpfdf[aobvs.columns[0]], _p)
            taplots += [
                mpf.make_addplot(mpfdf[aobvs.columns[0]], ylabel=aobv_name, color="black", width=1.5, panel=cpanel(), ylim=aobv_ylim),
                mpf.make_addplot(mpfdf[aobvs.columns[2]], color="silver", width=1, panel=cpanel(), ylim=aobv_ylim),
                mpf.make_addplot(mpfdf[aobvs.columns[3]], color="green", width=1, panel=cpanel(), ylim=aobv_ylim),
                mpf.make_addplot(mpfdf[aobvs.columns[4]], color="red", width=1.2, panel=cpanel(), ylim=aobv_ylim),
            ]
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel

        if clr_name in mpfdf_columns:
            _p = kwargs.pop("clr_percenty", 0.1)
            clr_ylim = ta_ylim(mpfdf[clr_name], _p)

            taplots += [mpf.make_addplot(mpfdf[clr_name], ylabel=clr_name, color="black", width=1.5, panel=cpanel(), ylim=clr_ylim)]
            if (1 - _p) * mpfdf[clr_name].min() < 0 and (1 + _p) * mpfdf[clr_name].max() > 0:
                taplots += [mpf.make_addplot(mpfdf["0"], color="gray", width=1.2, panel=cpanel(), ylim=clr_ylim)]
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel

        if rsi_name in mpfdf_columns:
            rsi_ylim = (0, 100)
            taplots += [
                mpf.make_addplot(mpfdf[rsi_name], ylabel=rsi_name, color=kwargs.pop("rsi_color", "black"), width=1.5, panel=cpanel(), ylim=rsi_ylim),
                mpf.make_addplot(mpfdf["20"], color="green", width=1, panel=cpanel(), ylim=rsi_ylim),
                mpf.make_addplot(mpfdf["50"], color="gray", width=0.8, panel=cpanel(), ylim=rsi_ylim),
                mpf.make_addplot(mpfdf["80"], color="red", width=1, panel=cpanel(), ylim=rsi_ylim),
            ]
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel
        
        if macd_name in mpfdf_columns:
            _p = kwargs.pop("macd_percenty", 0.15)
            macd_ylim = ta_ylim(mpfdf[macd_name], _p)
            taplots += [
                mpf.make_addplot(mpfdf[macd_name], ylabel=macd_name, color="black", width=1.5, panel=cpanel()),#, ylim=macd_ylim),
                mpf.make_addplot(mpfdf[macds.columns[-1]], color="blue", width=1.1, panel=cpanel()),#, ylim=macd_ylim),
                mpf.make_addplot(mpfdf[macds.columns[1]], type="bar", alpha=0.8, color="dimgray", width=0.8, panel=cpanel()),#, ylim=macd_ylim),
                mpf.make_addplot(mpfdf["0"], color="black", width=1.2, panel=cpanel()),#, ylim=macd_ylim),
            ]
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel            

        if zs_name in mpfdf_columns:
            _p = kwargs.pop("zascore_percenty", 0.2)
            zs_ylim = ta_ylim(mpfdf[zs_name], _p)
            taplots += [
                mpf.make_addplot(mpfdf[zs_name], ylabel=zs_name, color="black", width=1.5, panel=cpanel(), ylim=zs_ylim),
                mpf.make_addplot(mpfdf["-3"], color="red", width=1.2, panel=cpanel(), ylim=zs_ylim),
                mpf.make_addplot(mpfdf["-2"], color="orange", width=1, panel=cpanel(), ylim=zs_ylim),
                mpf.make_addplot(mpfdf["-1"], color="silver", width=1, panel=cpanel(), ylim=zs_ylim),
                mpf.make_addplot(mpfdf["0"], color="black", width=1.2, panel=cpanel(), ylim=zs_ylim),
                mpf.make_addplot(mpfdf["1"], color="silver", width=1, panel=cpanel(), ylim=zs_ylim),
                mpf.make_addplot(mpfdf["2"], color="orange", width=1, panel=cpanel(), ylim=zs_ylim),
                mpf.make_addplot(mpfdf["3"], color="red", width=1.2, panel=cpanel(), ylim=zs_ylim)
            ]
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel

        if squeeze_name in mpfdf_columns:
            _p = kwargs.pop("squeeze_percenty", 0.6)
            sqz_ylim = ta_ylim(mpfdf[squeeze_name], _p)
            taplots += [
                mpf.make_addplot(mpfdf[squeezes.columns[-4]], type="bar", color="lime", alpha=0.65, width=0.8, panel=cpanel(), ylim=sqz_ylim),
                mpf.make_addplot(mpfdf[squeezes.columns[-3]], type="bar", color="green", alpha=0.65, width=0.8, panel=cpanel(), ylim=sqz_ylim),
                mpf.make_addplot(mpfdf[squeezes.columns[-2]], type="bar", color="maroon", alpha=0.65, width=0.8, panel=cpanel(), ylim=sqz_ylim),
                mpf.make_addplot(mpfdf[squeezes.columns[-1]], type="bar", color="red", alpha=0.65, width=0.8, panel=cpanel(), ylim=sqz_ylim),
                mpf.make_addplot(mpfdf["0"], color="black", width=1.2, panel=cpanel(), ylim=sqz_ylim),
                mpf.make_addplot(mpfdf[squeezes.columns[4]], ylabel=squeeze_name, color="green", width=2, panel=cpanel(), ylim=sqz_ylim),
                mpf.make_addplot(mpfdf[squeezes.columns[5]], color="red", width=1.8, panel=cpanel(), ylim=sqz_ylim),
            ]
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel

        if tsig:
            _p = kwargs.pop("tsig_percenty", 0.23)
            treturn_ylim = ta_ylim(mpfdf["ACTRET_1"], _p)
            taplots += [
                mpf.make_addplot(mpfdf["ACTRET_1"], ylabel="Active % Return", type="bar", color="green", alpha=0.45, width=0.8, panel=cpanel(), ylim=treturn_ylim),
                mpf.make_addplot(pd.Series(mpfdf["ACTRET_1"].mean(), index=mpfdf["ACTRET_1"].index), color="blue", width=1, panel=cpanel(), ylim=treturn_ylim),
                mpf.make_addplot(mpfdf["0"], color="black", width=1, panel=cpanel(), ylim=treturn_ylim),
            ]
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel

            _p = kwargs.pop("cstreturn_percenty", 0.58)
            mpfdf["CUMACTRET_1"] = mpfdf["ACTRET_1"].cumsum()
            cumactret_ylim = ta_ylim(mpfdf["CUMACTRET_1"], _p)
            taplots += [
                mpf.make_addplot(mpfdf["CUMACTRET_1"], ylabel="Cum Trend Return", type="bar", color="silver", alpha=0.45, width=1, panel=cpanel(), ylim=cumactret_ylim),
                mpf.make_addplot(0.9 * buys * mpfdf["CUMACTRET_1"], type="scatter", marker="^", markersize=14, color="green", panel=cpanel(), ylim=cumactret_ylim),
                mpf.make_addplot(1.1 * sells * mpfdf["CUMACTRET_1"], type="scatter", marker="v", markersize=14, color="red", panel=cpanel(), ylim=cumactret_ylim),
                mpf.make_addplot(mpfdf["0"], color="black", width=1, panel=cpanel(), ylim=cumactret_ylim),
            ]            
            self.mpfchart["plot_ratios"] += common_plot_ratio # Required to add a new Panel

        # END: Custom TA Plots and Panels
        
        if self.verbose:
            additional_ta = []
            chart_title  = f"{chart_title} [{self.strategy.name}] (last {self.config['last']} bars)"
            chart_title += f"\nSince {mpfdf.index[0]} till {mpfdf.index[-1]}"
            if len(linreg_name) > 0: additional_ta.append(linreg_name)
            if len(midpoint_name) > 0: additional_ta.append(midpoint_name)
            if len(ohlc4_name) > 0: additional_ta.append(ohlc4_name)
            if len(additional_ta) > 0:
                chart_title += f"\nIncluding: {', '.join(additional_ta)}"

        if amat_sr:
            vlines_ = dict(vlines=amat_sr, alpha=0.1, colors="red")
        else:
            # Hidden because vlines needs valid arguments even if None 
            vlines_ = dict(vlines=mpfdf.index[0], alpha=0, colors="white")

        # Create Final Plot
        mpf.plot(mpfdf,
            title=chart_title,
            type=self.mpfchart["type"],
            style=self.mpfchart["style"],
            datetime_format="%-m/%-d/%Y",
            volume=self.config["volume"],
            figsize=self.mpfchart["figsize"],
            tight_layout=self.mpfchart["tight_layout"],
            scale_padding=self.mpfchart["scale_padding"],
            panel_ratios=self.mpfchart["plot_ratios"], # This key needs to be update above if adding more panels
            xrotation=self.mpfchart["xrotation"],
            update_width_config=self.mpfchart["width_config"],
            show_nontrading=self.mpfchart["non_trading"],
            vlines=vlines_,
            addplot=taplots
        )
        
        self._attribution()

Charting Example

Play with the parameters to see different charts and results

  • This is an example chart so it's not perfect. Enough to get started with common and uncommon plots.
  • There is a maximum of 10 Panels. In this example, panels 0 and 1 are reserved for Price and Volume respectively. *
In [11]:
# Used for example Trend Return Long Trend Below
macd_ = ta.macd(closedf)
macdh = macd_[macd_.columns[1]]

Chart(df,
    # style: which mplfinance chart style to use. Added "random" as an option.
    # rpad: how many bars to leave empty on the right of the chart
    style="yahoo", title=ticker, last=recent_bars(df), rpad=10,
    
    # Overlap Indicators
    linreg=True, midpoint=False, ohlc4=False, archermas=True,
    
    # Example Indicators with default parameters
    volume=True, rsi=True, clr=False, macd=True, zscore=False, squeeze=False, lazybear=False,

    # Archer OBV and OBV MAs (https://www.tradingview.com/script/Co1ksara-Trade-Archer-On-balance-Volume-Moving-Averages-v1/)
    archerobv=False,

    # Create trends and see their returns
    tsignals=True,
    # Example Trends or create your own. Trend must yield Booleans
    long_trend=ta.sma(closedf,10) > ta.sma(closedf,20), # trend: sma(close,10) > sma(close,20) [Default Example]
#     long_trend=closedf > ta.ema(closedf,5), # trend: close > ema(close,5)
#     long_trend=ta.sma(closedf,10) > ta.ema(closedf,50), # trend: sma(close,10) > ema(close,50)
#     long_trend=ta.increasing(ta.ema(closedf), 10), # trend: increasing(ema, 10)
#     long_trend=macdh > 0, # trend: macd hist > 0
#     long_trend=macd_[macd_.columns[0]] > macd_[macd_.columns[-1]], # trend: macd > macd signal
#     long_trend=ta.increasing(ta.sma(ta.rsi(closedf), 10), 5, asint=False), # trend: rising sma(rsi, 10) for the previous 5 periods
#     long_trend=ta.squeeze(highdf, lowdf, closedf, lazybear=True, detailed=True).SQZ_PINC > 0,
#     long_trend=ta.amat(closedf, 50, 200, mamode="sma").iloc[:,0], # trend: amat(50, 200) long signal using sma
    show_nontrading=False, # Intraday use if needed
    verbose=True, # More detail
)
Out [11]:
[i] Loaded SPY(7294, 34)
[+] Strategy: Common Price and Volume SMAs
[i] Indicator arguments: {'append': True}
[i] Multiprocessing 5 indicators with 7 chunks and 8/8 cpus.

100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:00<00:00, 65.28it/s]

[i] Total indicators: 5
[i] Columns added: 5
[i] Last Run: Thursday January 13, 2022, NYSE: 4:45:14, Local: 8:45:14 PST, Day 13/365 (4.00%)

Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas
Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI
Technical Analysis with Pandas TA v: 0.3.32b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta
Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance


<__main__.Chart at 0x13371d370>

Indicator Examples

  • Examples of simple and complex indicators. Most indicators return a Series, while a few return DataFrames.
  • All indicators can be called one of three ways. Either way, they return the result.

Three ways to use pandas_ta

  1. Stand Alone like TA-Lib ta.indicator(kwargs).
  2. As a DataFrame Extension like df.ta.indicator(kwargs). Where df is a DataFrame with columns named "open", "high", "low", "close, "volume" for simplicity.
  3. Similar to #2, but by calling: df.ta(kind="indicator", kwargs).

Cumulative Log Return

In [12]:
clr_ma_length = 8
clrdf = df.ta.log_return(cumulative=True, append=True)
clrmadf = ta.ema(clrdf, length=clr_ma_length)
clrxdf = pd.DataFrame({f"{clrdf.name}": clrdf, f"{clrmadf.name}({clrdf.name})": clrmadf})
clrxdf.tail(recent_bars(df)).plot(figsize=ind_size, color=colors("BkBl"), linewidth=1, title=ctitle(clrdf.name, ticker=ticker, length=recent_bars(df)), grid=True)
Out [12]:
<AxesSubplot:title={'center':'SPY: CUMLOGRET_1 from 2021-01-15 00:00:00 to 2022-01-13 00:00:00 (252)'}, xlabel='date'>

MACD

In [13]:
macddf = df.ta.macd(fast=8, slow=21, signal=9, min_periods=None, append=True)
macddf[[macddf.columns[0], macddf.columns[2]]].tail(recent_bars(df)).plot(figsize=(16, 2), color=colors("BkBl"), linewidth=1.3)
macddf[macddf.columns[1]].tail(recent_bars(df)).plot.area(figsize=ind_size, stacked=False, color=["silver"], linewidth=1, title=ctitle(macddf.name, ticker=ticker, length=recent_bars(df)), grid=True).axhline(y=0, color="black", lw=1.1)
Out [13]:
<matplotlib.lines.Line2D at 0x1356eceb0>

ZScore

In [14]:
zscoredf = df.ta.zscore(length=30, append=True)
zcolors = ["darkgreen", "green", "silver", "silver", "red", "maroon", "black"]
zcols = df[["-4", "-3", "-2", "2", "3", "4", zscoredf.name]].tail(recent_bars(df))
zcols.plot(figsize=ind_size, color=zcolors, linewidth=1.2, title=ctitle(zscoredf.name, ticker=ticker, length=recent_bars(df)), grid=True).axhline(y=0, color="black", lw=1.1)
Out [14]:
<matplotlib.lines.Line2D at 0x135764ee0>
In [15]:
# Now Volume Z Score
zvscoredf = df.ta.zscore(close="volume", length=30, prefix="VOL", append=True)
zcolors = ["darkgreen", "green", "silver", "silver", "red", "maroon", "black"]
zvcols = df[["-4", "-3", "-2", "2", "3", "4", zvscoredf.name]].tail(recent_bars(df))
zvcols.plot(figsize=ind_size, color=zcolors, linewidth=1.2, title=ctitle(zvscoredf.name, ticker=ticker, length=recent_bars(df)), grid=True).axhline(y=0, color="black", lw=1.1)
Out [15]:
<matplotlib.lines.Line2D at 0x13558ea60>

New Features

Squeeze Indicator (John Carter and Lazybear Versions)

Squeeze Indicator (squeeze)

In [16]:
# help(ta.squeeze)
In [17]:
Chart(df, style="yahoo", title=ticker, verbose=False,
    last=recent_bars(df), rpad=10, clr=True, squeeze=True,
    show_nontrading=False, # Intraday use if needed
)
Out [17]:
Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas
Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI
Technical Analysis with Pandas TA v: 0.3.32b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta
Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance


<__main__.Chart at 0x135831b80>

Lazybear's TradingView Squeeze

In [18]:
Chart(df, style="yahoo", title=ticker, verbose=False,
    last=recent_bars(df), rpad=10, clr=True, squeeze=True, lazybear=True,
    show_nontrading=False, # Intraday use if needed
)
Out [18]:
Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas
Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI
Technical Analysis with Pandas TA v: 0.3.32b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta
Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance


<__main__.Chart at 0x13c82d040>

Archer Moving Averages Trends

Archer Moving Average Trends (amat) returns the long and short run trends of fast and slow moving averages.

  • The pink background, on the Price chart, is when Archer MAs are bearish. Conversely, a white background is bullish
In [19]:
Chart(df, style="yahoo", title=ticker, verbose=False,
    last=recent_bars(df), rpad=10,
    volume=True, midpoint=False, ohlc4=False,
    rsi=False, clr=True, macd=False, zscore=False, squeeze=False, lazybear=False,
    archermas=True, archerobv=False,
    show_nontrading=False, # Intraday use if needed
)
Out [19]:
Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas
Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI
Technical Analysis with Pandas TA v: 0.3.32b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta
Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance


<__main__.Chart at 0x134e8e700>

Archer On Balance Volume

Archer On Balance Volume (aobv) returns a DataFrame of OBV, OBV min and max, fast and slow MAs of OBV, and the long and short run trends of the two OBV MAs.

  • On the chart below, only OBV, OBV min, fast and slow OBV MAs.
  • Not on the chart are: OBV LR and OBV SR trends.
In [20]:
Chart(df, style="yahoo", title=ticker, verbose=False,
    last=recent_bars(df), rpad=10,
    volume=True, midpoint=False, ohlc4=False,
    rsi=False, clr=True, macd=False, zscore=False, squeeze=False, lazybear=False,
    archermas=False, archerobv=True,
    show_nontrading=False, # Intraday use if needed
)
Out [20]:
Pandas v: 1.3.0 [pip install pandas] https://github.com/pandas-dev/pandas
Data from AlphaVantage v: 1.0.19 [pip install alphaVantage-api] http://www.alphavantage.co https://github.com/twopirllc/AlphaVantageAPI
Technical Analysis with Pandas TA v: 0.3.32b0 [pip install pandas_ta] https://github.com/twopirllc/pandas-ta
Charts by Matplotlib Finance v: 0.12.7a17 [pip install mplfinance] https://github.com/matplotlib/mplfinance


<__main__.Chart at 0x1357f4790>

Disclaimer

  • 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.

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