TA Analysis with Pandas TA and AI/ML

This is a Work in Progress and subject to change!
Contributions are welcome and accepted!
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.

Required Packages

Uncomment the packages you need to install or are missing

In [1]:

#!pip install numpy
#!pip install pandas
#!pip install mplfinance
#!pip install pandas-datareader
#!pip install requests_cache
#!pip install tqdm
#!pip install alphaVantage-api # Required for Watchlist

In [2]:

%pylab inline
import datetime as dt
import random as rnd
from sys import float_info as sflt

from tqdm import tqdm

import numpy as np
import pandas as pd
pd.set_option("max_rows", 100)
pd.set_option("max_columns", 20)

import mplfinance as mpf
import pandas_ta as ta

from tqdm.notebook import trange, tqdm

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

print(f"Numpy v{np.__version__}")
print(f"Pandas v{pd.__version__}")
print(f"mplfinance v{mpf.__version__}")
print(f"\nPandas TA v{ta.version}\nTo install the Latest Version:\n$ pip install -U git+https://github.com/twopirllc/pandas-ta\n")
%matplotlib inline

Populating the interactive namespace from numpy and matplotlib
Numpy v1.20.3
Pandas v1.3.0
mplfinance v0.12.7a17

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

MISC Function(s)

In [3]:

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]

Data Collection

In [4]:

tf = "D"
tickers = ["SPY", "QQQ", "AAPL", "TSLA", "BTC-USD"]
watch = Watchlist(tickers, tf=tf, ds_name="yahoo", timed=True)
# watch.study = ta.CommonStudy # If you have a Custom Study, you can use it here.
watch.load(tickers, analyze=True, verbose=False)

[!] Loading All: SPY, QQQ, AAPL, TSLA, BTC-USD
[+] Downloading[yahoo]: SPY[D]
[+] Saving: /Users/kj/av_data/SPY_D.csv
[i] Runtime: 707.3915 ms (0.7074 s)
[+] Downloading[yahoo]: QQQ[D]
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Runtime: 696.6895 ms (0.6967 s)
[+] Downloading[yahoo]: AAPL[D]
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Runtime: 856.9906 ms (0.8570 s)
[+] Downloading[yahoo]: TSLA[D]
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Runtime: 719.1806 ms (0.7192 s)
[+] Downloading[yahoo]: BTC-USD[D]
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Runtime: 811.2178 ms (0.8112 s)

Asset Selection

In [5]:

ticker = tickers[0] # change tickers by changing the index
print(f"{ticker} {watch.data[ticker].shape}\nColumns: {', '.join(list(watch.data[ticker].columns))}")

SPY (7299, 12)
Columns: open, high, low, close, volume, dividends, stock splits, SMA_10, SMA_20, SMA_50, SMA_200, VOL_SMA_20

Trim it

In [6]:

duration = "1y"
asset = watch.data[ticker]
recent = recent_bars(asset, duration)
asset.columns = asset.columns.str.lower()
asset.drop(columns=["dividends", "split"], errors="ignore", inplace=True)
asset = asset.copy().tail(recent)
asset

Out [6]:

	open	high	low	close	volume	stock splits	sma_10	sma_20	sma_50	sma_200	vol_sma_20
date
2021-01-25	378.623058	379.708564	373.481570	379.333588	70402000	0	375.540128	371.941592	363.642487	325.851111	63120030.0
2021-01-26	380.340140	380.774354	378.494744	378.741455	42665300	0	376.043420	372.671364	364.207632	326.389357	63930400.0
2021-01-27	375.218403	375.317093	367.116410	369.484833	123351100	0	375.613156	372.781891	364.655645	326.893695	68147935.0
2021-01-28	371.409193	376.905930	370.945404	372.662506	94198100	0	375.400000	373.086334	365.071107	327.374311	70173815.0
2021-01-29	370.688763	371.715090	363.425564	365.201904	126765100	0	374.572037	372.991595	365.249507	327.847003	74039305.0
...	...	...	...	...	...	...	...	...	...	...	...
2022-01-14	461.190002	465.089996	459.899994	464.720001	95849600	0	469.319998	469.606500	465.989207	438.476059	78776895.0
2022-01-18	459.739990	459.959991	455.309998	456.489990	109709100	0	467.197998	469.437500	465.813499	438.746869	77486770.0
2022-01-19	458.130005	459.609985	451.459991	451.750000	109357600	0	464.617999	469.275999	465.510704	438.995168	77597910.0
2022-01-20	453.750000	458.739990	444.500000	446.750000	122379700	0	462.454999	468.460500	465.099938	439.216139	80226580.0
2022-01-21	445.559998	448.059998	437.950012	437.980011	201913500	0	459.459000	466.975000	464.544664	439.383707	87377745.0

252 rows × 11 columns

Trend Creation

A Trend is the result of some calculation or condition of one or more indicators. For simplicity, a Trend is either True or 1 and No Trend is False or 0. Using the Hello World of Trends, the Golden/Death Cross, it's Trend is Long when long = ma(close, 50) > ma(close, 200) and Short when short = ma(close, 50) < ma(close, 200) .

In [7]:

# Example Long Trends
# long = ta.sma(asset.close, 50) > ta.sma(asset.close, 200) # SMA(50) > SMA(200) "Golden/Death Cross"
# long = ta.sma(asset.close, 10) > ta.sma(asset.close, 20) # SMA(10) > SMA(20)
long = ta.ema(asset.close, 8) > ta.ema(asset.close, 21) # EMA(8) > EMA(21)
# long = ta.increasing(ta.ema(asset.close, 50))
# long = ta.macd(asset.close).iloc[:,1] > 0 # MACD Histogram is positive
# long = ta.amat(asset.close, 50, 200).AMATe_LR_2  # Long Run of AMAT(50, 200) with lookback of 2 bars

# long &= ta.increasing(ta.ema(asset.close, 50), 2) # Uncomment for further long restrictions, in this case when EMA(50) is increasing/sloping upwards
# long = 1 - long # uncomment to create a short signal of the trend

asset.ta.ema(length=8, sma=False, append=True)
asset.ta.ema(length=21, sma=False, append=True)
asset.ta.ema(length=50, sma=False, append=True)
asset.ta.percent_return(append=True)
print("TA Columns Added:")
asset[asset.columns[5:]].tail()

Out [7]:

TA Columns Added:

	stock splits	sma_10	sma_20	sma_50	sma_200	vol_sma_20	EMA_8	EMA_21	EMA_50	PCTRET_1
date
2022-01-14	0	469.319998	469.606500	465.989207	438.476059	78776895.0	467.864562	468.366344	464.226269	0.000409
2022-01-18	0	467.197998	469.437500	465.813499	438.746869	77486770.0	465.336880	467.286675	463.922886	-0.017710
2022-01-19	0	464.617999	469.275999	465.510704	438.995168	77597910.0	462.317573	465.874250	463.445517	-0.010384
2022-01-20	0	462.454999	468.460500	465.099938	439.216139	80226580.0	458.858112	464.135682	462.790791	-0.011068
2022-01-21	0	459.459000	466.975000	464.544664	439.383707	87377745.0	454.218534	461.757894	461.817820	-0.019631

Trend Signals

Given a Trend, Trend Signals returns the Trend, Trades, Entries and Exits as boolean integers. When asbool=True, it returns Trends, Entries and Exits as boolean values which is helpful when combined with the vectorbt backtesting package.

In [8]:

trendy = asset.ta.tsignals(long, asbool=False, append=True)
trendy.tail()

Out [8]:

	TS_Trends	TS_Trades	TS_Entries	TS_Exits
date
2022-01-14	0	-1	0	1
2022-01-18	0	0	0	0
2022-01-19	0	0	0	0
2022-01-20	0	0	0	0
2022-01-21	0	0	0	0

Trend Entries & Exits & Trade Table

This is a simple way to reduce the Asset DataFrame to a Trade Table with Dates, Signals, and Entries and Exits. Gives you an idea what to expect before running through a backtester such as vectorbt.

In [9]:

entries = trendy.TS_Entries * asset.close
entries = entries[~np.isclose(entries, 0)]
entries.dropna(inplace=True)
entries.name = "Entry"

exits = trendy.TS_Exits * asset.close
exits = exits[~np.isclose(exits, 0)]
exits.dropna(inplace=True)
exits.name = "Exit"

total_trades = trendy.TS_Trades.abs().sum()
rt_trades = int(trendy.TS_Trades.abs().sum() // 2)

all_trades = trendy.TS_Trades.copy().fillna(0)
all_trades = all_trades[all_trades != 0]

trades = pd.DataFrame({
    "Signal": all_trades,
    entries.name: entries,
    exits.name: exits
})

# Show some stats if there is an active trade (when there is an odd number of round trip trades)
if total_trades % 2 != 0:
    unrealized_pnl = asset.close.iloc[-1] - entries.iloc[-1]
    unrealized_pnl_pct_change = 100 * ((asset.close.iloc[-1] / entries.iloc[-1]) - 1)
    print("Current Trade:")
    print(f"Price Entry | Last:\t{entries.iloc[-1]:.4f} | {asset.close.iloc[-1]:.4f}")
    print(f"Unrealized PnL | %:\t{unrealized_pnl:.4f} | {unrealized_pnl_pct_change:.4f}%")
print(f"\nTrades Total | Round Trip:\t{total_trades} | {rt_trades}")
print(f"Trade Coverage: {100 * asset.TS_Trends.sum() / asset.shape[0]:.2f}%")

trades

Out [9]:

Trades Total | Round Trip:	12 | 6
Trade Coverage: 77.78%

	Signal	Entry	Exit
date
2021-02-23	1	382.402618	NaN
2021-03-04	-1	NaN	371.744690
2021-03-10	1	384.455292	NaN
2021-05-19	-1	NaN	406.783295
2021-05-20	1	411.159454	NaN
2021-09-17	-1	NaN	439.854675
2021-10-15	1	444.309021	NaN
2021-12-01	-1	NaN	448.922821
2021-12-08	1	467.876221	NaN
2021-12-20	-1	NaN	454.980011
2021-12-21	1	463.059998	NaN
2022-01-14	-1	NaN	464.720001

In [ ]:

Visualization

Chart Display Strings

In [10]:

extime = ta.get_time(to_string=True)
first_date, last_date = asset.index[0], asset.index[-1]
f_date = f"{first_date.day_name()} {first_date.month}-{first_date.day}-{first_date.year}"
l_date = f"{last_date.day_name()} {last_date.month}-{last_date.day}-{last_date.year}"
last_ohlcv = f"Last OHLCV: ({asset.iloc[-1].open:.4f}, {asset.iloc[-1].high:.4f}, {asset.iloc[-1].low:.4f}, {asset.iloc[-1].close:.4f}, {int(asset.iloc[-1].volume)})"
ptitle = f"\n{ticker} [{tf} for {duration}({recent} bars)] from {f_date} to {l_date}\n{last_ohlcv}\n{extime}"

Trade Chart

In [11]:

# chart = asset["close"] #asset[["close", "SMA_10", "SMA_20", "SMA_50", "SMA_200"]]
# chart = asset[["close", "SMA_10", "SMA_20"]]
chart = asset[["close", "EMA_8", "EMA_21", "EMA_50"]]
chart.plot(figsize=(16, 10), color=colors("BkGrOrRd"), title=ptitle, grid=True)

Out [11]:

<AxesSubplot:title={'center':'\nSPY [D for 1y(252 bars)] from Monday 1-25-2021 to Friday 1-21-2022\nLast OHLCV: (445.5600, 448.0600, 437.9500, 437.9800, 201913500)\nSunday January 23, 2022, NYSE: 11:44:28, Local: 15:44:28 PST, Day 23/365 (6.00%)'}, xlabel='date'>

Long and Short Trends

Trends are either a Trend (1) or No Trend (0) depending on the Trend passed into *Trend Signals

In [12]:

long_trend = trendy.TS_Trends
short_trend = 1 - long_trend

long_trend.plot(figsize=(16, 0.85), kind="area", stacked=True, color=colors()[0], alpha=0.25) # Green Area
short_trend.plot(figsize=(16, 0.85), kind="area", stacked=True, color=colors()[1], alpha=0.25) # Red Area

Out [12]:

<AxesSubplot:xlabel='date'>

Trades or Trade Signals

The Trades are either Enter (1) or Exit (-1) or No Position/Action (0). These are based on the Trend passed into Trend Signals whether they are Long or Short Trends.

In [13]:

trendy.TS_Trades.plot(figsize=(16, 1.5), color=colors("BkBl")[0], grid=True)

Out [13]:

<AxesSubplot:xlabel='date'>

Active Returns

Active Returns are returns made during the course of the Trend. They are simply the product of the returns and the Trend

In [14]:

asset["ACTRET_1"] = trendy.TS_Trends.shift(1) * asset.PCTRET_1
asset[["PCTRET_1", "ACTRET_1"]].plot(figsize=(16, 3), color=colors("GyOr"), alpha=1, grid=True).axhline(0, color="black")

Out [14]:

<matplotlib.lines.Line2D at 0x130230a60>

Buy and Hold Returns (PCTRET_1) vs. Cum. Active Returns (ACTRET_1)

In [15]:

((asset[["PCTRET_1", "ACTRET_1"]] + 1).cumprod() - 1).plot(figsize=(16, 3), kind="area", stacked=False, color=colors("GyOr"), title="B&H vs. Cum. Active Returns", alpha=.4, grid=True).axhline(0, color="black")

Out [15]:

<matplotlib.lines.Line2D at 0x1304c2610>

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.