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: 463.0497 ms (0.4630 s)
[+] Downloading[yahoo]: QQQ[D]
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Runtime: 432.8772 ms (0.4329 s)
[+] Downloading[yahoo]: AAPL[D]
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Runtime: 435.9255 ms (0.4359 s)
[+] Downloading[yahoo]: TSLA[D]
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Runtime: 444.1443 ms (0.4441 s)
[+] Downloading[yahoo]: BTC-USD[D]
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Runtime: 435.2471 ms (0.4352 s)

Select an Asset

In [5]:

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

SPY (7081, 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"
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	Stock Splits	SMA_10	SMA_20	SMA_50	SMA_200	VOL_SMA_20	EMA_8	EMA_21	EMA_50
date
2021-03-08	0.0	0	384.154999	387.410500	381.464001	345.220762	89023860.0	383.250286	384.533350	379.947811
2021-03-09	0.0	0	384.122000	387.243500	381.856001	345.700730	92778490.0	384.121336	384.773046	380.231034
2021-03-10	0.0	0	383.903000	387.209999	382.267601	346.189984	96483975.0	385.334370	385.210041	380.597660
2021-03-11	0.0	0	385.023001	387.382500	382.694800	346.681016	97827625.0	387.155621	385.966401	381.104811
2021-03-12	0.0	0	386.393002	387.550000	383.146801	347.152728	98920260.0	388.689927	386.702182	381.612857

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
2021-03-08	0.000000	0.000000	0	0
2021-03-09	0.000000	0.000000	0	1
2021-03-10	0.006205	0.006205	1	0
2021-03-11	0.016293	0.010088	1	0
2021-03-12	0.017639	0.001346	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 13, 2021, NYSE: 12:21:03, Local: 16:21:03 PST, Day 72/365 (20.0%)'}, 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
2020-04-09	1	274.705811	NaN
2020-09-10	-1	NaN	331.147217
2020-10-02	1	332.423004	NaN
2020-10-27	-1	NaN	336.784393
2020-11-04	1	342.081818	NaN
2021-03-02	-1	NaN	386.540009
2021-03-09	1	387.170013	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 3-13-2020 to Friday 3-12-2021\nLast OHLCV: (392.0700, 394.2100, 391.2000, 394.0600, 64608100)\nSaturday March 13, 2021, NYSE: 12:21:03, Local: 16:21:03 PST, Day 72/365 (20.0%)'}, 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.