Strategy 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.strategy = ta.CommonStrategy # If you have a Custom Strategy, you can use it here.
watch.load(tickers, analyze=True, verbose=False)

[!] Loading All: SPY, QQQ, AAPL, TSLA, BTC-USD
[i] Loaded SPY[D]: SPY_D.csv
[i] Runtime: 736.1327 ms (0.7361 s)
[+] Downloading[yahoo]: QQQ[D]
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Runtime: 697.6427 ms (0.6976 s)
[+] Downloading[yahoo]: AAPL[D]
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Runtime: 679.9694 ms (0.6800 s)
[+] Downloading[yahoo]: TSLA[D]
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Runtime: 738.0908 ms (0.7381 s)
[+] Downloading[yahoo]: BTC-USD[D]
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Runtime: 755.3894 ms (0.7554 s)

Asset Selection

In [16]:

ticker = tickers[2] # change tickers by changing the index
print(f"{ticker} {watch.data[ticker].shape}\nColumns: {', '.join(list(watch.data[ticker].columns))}")

AAPL (10361, 12)
Columns: Open, High, Low, Close, Volume, Dividends, Stock Splits, SMA_10, SMA_20, SMA_50, SMA_200, VOL_SMA_20

Trim it

In [17]:

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 [17]:

	open	high	low	close	volume	stock splits	sma_10	sma_20	sma_50	sma_200	vol_sma_20
Date
2021-01-15	127.990959	129.422138	126.221866	126.361008	111598500	0.0	128.677734	129.754095	123.478919	103.135757	1.127449e+08
2021-01-19	126.997087	127.921397	126.162237	127.046783	90757300	0.0	128.520701	129.710862	123.738876	103.468593	1.125648e+08
2021-01-20	127.871707	131.678242	127.762380	131.221054	104319500	0.0	128.622076	129.977717	124.001358	103.826647	1.081537e+08
2021-01-21	132.980205	138.814235	132.771485	136.031387	120150900	0.0	129.642783	130.407070	124.362730	104.182751	1.080986e+08
2021-01-22	135.445012	138.993146	134.192737	138.217926	114459400	0.0	130.452789	130.764368	124.814942	104.553542	1.053764e+08
...	...	...	...	...	...	...	...	...	...	...	...
2022-01-07	172.889999	174.139999	171.029999	172.169998	86580100	0.0	177.556999	176.122500	164.787658	145.042709	1.032868e+08
2022-01-10	169.080002	172.500000	168.169998	172.190002	106765600	0.0	176.742999	175.759500	165.184505	145.300422	1.028636e+08
2022-01-11	172.320007	175.179993	170.820007	175.080002	76138300	0.0	176.322000	175.726500	165.694471	145.571689	9.900872e+07
2022-01-12	176.119995	177.179993	174.820007	175.529999	74711100	0.0	175.937000	175.786500	166.230213	145.852622	9.577525e+07
2022-01-13	175.779999	176.619995	174.529999	175.199997	29711051	0.0	175.637000	175.581499	166.738185	146.120707	9.070764e+07

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 [18]:

# 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 [18]:

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
2022-01-07	0.0	177.556999	176.122500	164.787658	145.042709	1.032868e+08	175.550651	174.090649	166.893160	0.000988
2022-01-10	0.0	176.742999	175.759500	165.184505	145.300422	1.028636e+08	174.803840	173.917863	167.100879	0.000116
2022-01-11	0.0	176.322000	175.726500	165.694471	145.571689	9.900872e+07	174.865210	174.023512	167.413786	0.016784
2022-01-12	0.0	175.937000	175.786500	166.230213	145.852622	9.577525e+07	175.012941	174.160465	167.732069	0.002570
2022-01-13	0.0	175.637000	175.581499	166.738185	146.120707	9.070764e+07	175.054509	174.254968	168.024929	-0.001880

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 [19]:

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

Out [19]:

	TS_Trends	TS_Trades	TS_Entries	TS_Exits
Date
2022-01-07	1	0	0	0
2022-01-10	1	0	0	0
2022-01-11	1	0	0	0
2022-01-12	1	0	0	0
2022-01-13	1	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 [20]:

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 [20]:

Current Trade:
Price Entry | Last:	148.5432 | 175.2000
Unrealized PnL | %:	26.6568 | 17.9455%

Trades Total | Round Trip:	5 | 2
Trade Coverage: 59.13%

	Signal	Entry	Exit
Date
2021-04-06	1	125.624153	NaN
2021-05-06	-1	NaN	129.137772
2021-06-14	1	130.094925	NaN
2021-09-16	-1	NaN	148.573151
2021-10-19	1	148.543198	NaN

In [ ]:

Visualization

Chart Display Strings

In [21]:

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 [22]:

# 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 [22]:

<AxesSubplot:title={'center':'\nAAPL [D for 1y(252 bars)] from Friday 1-15-2021 to Thursday 1-13-2022\nLast OHLCV: (175.7800, 176.6200, 174.5300, 175.2000, 29711051)\nThursday January 13, 2022, NYSE: 4:44:17, Local: 8:44:17 PST, Day 13/365 (4.00%)'}, 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 [23]:

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 [23]:

<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 [24]:

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

Out [24]:

<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 [25]:

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 [25]:

<matplotlib.lines.Line2D at 0x13447fd30>

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

In [26]:

((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 [26]:

<matplotlib.lines.Line2D at 0x13445c610>

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.