pandas-ta/examples/AIExample.ipynb

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 alphaVantage-api # Required for Watchlist

In [2]:

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

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 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 v1.20.1
Pandas v1.2.2
mplfinance v0.12.7a5

Pandas TA v0.2.64b0
To install the Latest Version:
$ pip install -U git+https://github.com/twopirllc/pandas-ta

MISC Functions

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]

Collect some Data

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
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: 503.8784 ms (0.5039 s)
[+] Downloading[yahoo]: QQQ[D]
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Runtime: 450.2592 ms (0.4503 s)
[+] Downloading[yahoo]: AAPL[D]
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Runtime: 475.9419 ms (0.4759 s)
[+] Downloading[yahoo]: TSLA[D]
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Runtime: 454.6816 ms (0.4547 s)
[+] Downloading[yahoo]: BTC-USD[D]
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Runtime: 447.3887 ms (0.4474 s)

Select an Asset

In [5]:

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

SPY (7100, 12)
Columns: open, high, low, close, volume, dividends, split, SMA_10, SMA_20, SMA_50, SMA_200, VOL_SMA_20

Trim it

In [6]:

duration = "1y"
recent = recent_bars(watch.data[ticker], duration)
asset = watch.data[ticker].copy().tail(recent)

Create a Trend

In [7]:

# Example Long Trends
# 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

asset.ta.ema(length=8, append=True)
asset.ta.ema(length=21, append=True)
asset.ta.ema(length=50, append=True)
asset[asset.columns[5:]].tail()

Out [7]:

	dividends	split	SMA_10	SMA_20	SMA_50	SMA_200	VOL_SMA_20	EMA_8	EMA_21	EMA_50
date
2021-04-05	0.0	0	394.909998	392.709276	387.393192	352.643440	97120120.0	397.670340	393.120112	386.866944
2021-04-06	0.0	0	396.262997	393.991583	387.882991	353.139745	94073315.0	399.548041	394.301919	387.621965
2021-04-07	0.0	0	397.971997	395.025780	388.352087	353.647168	91192240.0	401.112920	395.419017	388.365810
2021-04-08	0.0	0	400.071997	396.036369	388.871745	354.154454	88602355.0	402.758935	396.610015	389.156170
2021-04-09	0.0	0	402.250995	396.998604	389.637791	354.669523	87353990.0	404.699170	397.962740	390.032006

Calculate Trend Returns from the long trend

In [8]:

trendy = asset.ta.trend_return(trend=long, asbool=False, append=True)
trendy.tail() # Third Column is the long trend; binary sequences

Out [8]:

	TR_LOGRET_1	TR_CUMLOGRET_1	TR_Trends	TR_Trades	TR_Entries	TR_Exits
date
2021-04-05	0.014251	0.051645	1	0	0	0
2021-04-06	-0.000591	0.051055	1	0	0	0
2021-04-07	0.001157	0.052211	1	0	0	0
2021-04-08	0.004736	0.056947	1	0	0	0
2021-04-09	0.007244	0.064191	1	0	0	0

Simple Price Chart

In [9]:

extime = ta.get_time(to_string=True)
chart_ = asset[["close", "EMA_8", "EMA_21", "EMA_50"]]
chart_.plot(figsize=(16, 10), color=colors("BkGrOrRd"), title=f"{ticker} {extime}", grid=True)

Out [9]:

<AxesSubplot:title={'center':'SPY Saturday April 10, 2021, NYSE: 12:36:10, Local: 16:36:10 PDT, Day 100/365 (27.0%)'}, xlabel='date'>

Entries & Exits

In [10]:

entries = trendy.TR_Entries.astype(int) * asset.close
entries = entries[~np.isclose(entries, 0)]
entries.name = "Entry"

exits = trendy.TR_Exits.astype(int) * asset.close
exits = exits[~np.isclose(exits, 0)]
exits.name = "Exit"

total_trades = trendy.TR_Trades.abs().sum()
rt_trades = int(trendy.TR_Trades.abs().sum() // 2)
print(f"Total Trades: {total_trades}\t\tRT Trades: {rt_trades}")

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

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

Out [10]:

Total Trades: 7		RT Trades: 3

	Signal	Entry	Exit
date
2020-05-07	1	283.139404	NaN
2020-09-10	-1	NaN	330.066162
2020-10-02	1	331.337799	NaN
2020-10-27	-1	NaN	335.684937
2020-11-04	1	340.965088	NaN
2021-03-02	-1	NaN	385.278137
2021-03-09	1	385.906067	NaN

Chart Display Strings

In [11]:

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 [12]:

# 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)
entries.plot(figsize=(16, 10), color=colors("FcLi")[1], marker="^", markersize=12, alpha=0.8)
exits.plot(figsize=(16, 10), color=colors("FcLi")[0], marker="v", markersize=12, alpha=0.8, grid=True)

Out [12]:

<AxesSubplot:title={'center':'\nSPY [D for 1y(252 bars)] from Thursday 4-9-2020 to Friday 4-9-2021\nLast OHLCV: (408.3900, 411.6700, 408.2600, 411.4900, 61060100)\nSaturday April 10, 2021, NYSE: 12:36:10, Local: 16:36:10 PDT, Day 100/365 (27.0%)'}, xlabel='date'>

Active Returns and Cumulative Active Returns

In [13]:

trendy.iloc[:,:2].plot(figsize=(16, 3), color=colors("BkBl")[::-1])
trendy.TR_CUMLOGRET_1.plot(figsize=(16, 3), kind="area", stacked=False, color=colors("SvGy")[0], alpha=0.25, grid=True)

Out [13]:

<AxesSubplot:xlabel='date'>

Total Return

In [14]:

capital = 10000

total_return = trendy.TR_CUMLOGRET_1.cumsum() * capital
positive_return = total_return[total_return > 0]
negative_return = total_return[total_return <= 0]
trdf = pd.DataFrame({"tr+": positive_return, "tr-": negative_return})
trdf.plot(figsize=(16, 5), color=colors(), kind="area", stacked=False, alpha=0.25, grid=True)

Out [14]:

<AxesSubplot:xlabel='date'>

Long and Short Trends

In [15]:

long_trend = (trendy.iloc[:,2] > 0).astype(int)
short_trend = (1 - long_trend).astype(int)

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

Out [15]:

<AxesSubplot:xlabel='date'>

AI Analysis

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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.