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 v0.2.49b0
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: 450.8733 ms (0.4509 s)
[+] Downloading[yahoo]: QQQ[D]
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Runtime: 438.7223 ms (0.4387 s)
[+] Downloading[yahoo]: AAPL[D]
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Runtime: 443.3815 ms (0.4434 s)
[+] Downloading[yahoo]: TSLA[D]
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Runtime: 446.6432 ms (0.4466 s)
[+] Downloading[yahoo]: BTC-USD[D]
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Runtime: 454.7926 ms (0.4548 s)

Select an Asset

In [5]:

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

SPY (7086, 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
2021-03-15	0.000	0	385.812372	386.472707	382.382806	346.504071	100066335.0	389.130999	386.319421	380.945454
2021-03-16	0.000	0	386.746310	386.652617	382.821967	346.979575	101165615.0	390.350227	387.073794	381.481613
2021-03-17	0.000	0	388.325140	386.895322	383.389508	347.455754	103142185.0	391.597540	387.881916	382.049516
2021-03-18	0.000	0	389.798315	386.933197	383.791193	347.890677	105912170.0	391.287419	388.092832	382.369221
2021-03-19	1.278	0	390.508554	386.969362	384.134183	348.301813	107389010.0	390.885773	388.218939	382.648076

Calculate Trend Returns from the long trend

In [8]:

trendy = asset.ta.trend_return(trend=long, cumulative=True, trade_offset=-1, append=True)
trendy.tail() # Third Column is the long trend; binary sequences

Out [8]:

	CLTR	TR_LOGRET	CLTR_Trends	CLTR_Trades
date
2021-03-15	0.023585	0.005946	1	0
2021-03-16	0.022323	-0.001262	1	0
2021-03-17	0.025727	0.003404	1	0
2021-03-18	0.011071	-0.014657	1	0
2021-03-19	0.009219	-0.001852	1	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 March 20, 2021, NYSE: 13:09:30, Local: 17:09:30 PDT, Day 79/365 (22.0%)'}, xlabel='date'>

Entries & Exits

In [10]:

entries = (trendy.iloc[:,-1] > 0).astype(int) * asset.close
entries[entries < 0.0001] = np.nan
entries.name = "Entry"


exits = (trendy.iloc[:,-1] < 0).astype(int) * asset.close
exits[exits < 0.0001] = np.nan
exits.name = "Exit"

total_trades = trendy.iloc[:,-1].abs().sum()
print(f"Total Trades: {total_trades}")

all_trades = trendy.iloc[:,-1].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

	Signal	Entry	Exit
date
2020-04-17	1	282.115784	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 Friday 3-20-2020 to Friday 3-19-2021\nLast OHLCV: (389.8800, 391.5700, 387.1500, 389.4800, 112777800)\nSaturday March 20, 2021, NYSE: 13:09:30, Local: 17:09:30 PDT, Day 79/365 (22.0%)'}, xlabel='date'>

Trend Returns and Cumulative Trend Returns

In [13]:

cltr = trendy.iloc[:,0]
tr = trendy.iloc[:,1]

trendy.iloc[:,:2].plot(figsize=(16, 3), color=colors("BkBl"))
cltr.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 = cltr.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.