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]
[+] yf | SPY(7310, 7): 3552.4306 ms (3.5524 s)
[+] Saving: /Users/kj/av_data/SPY_D.csv
[i] Analysis Time: 33.4526 ms (0.0335 s)
[+] Downloading[yahoo]: QQQ[D]
[+] yf | QQQ(5768, 7): 3149.0533 ms (3.1491 s)
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Analysis Time: 3.2938 ms (0.0033 s)
[+] Downloading[yahoo]: AAPL[D]
[+] yf | AAPL(10377, 7): 3140.3905 ms (3.1404 s)
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Analysis Time: 3.6440 ms (0.0036 s)
[+] Downloading[yahoo]: TSLA[D]
[+] yf | TSLA(2924, 7): 2794.6956 ms (2.7947 s)
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Analysis Time: 2.8422 ms (0.0028 s)
[+] Downloading[yahoo]: BTC-USD[D]
[+] yf | BTC-USD(2701, 7): 3494.6843 ms (3.4947 s)
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Analysis Time: 3.6090 ms (0.0036 s)

Asset Selection

In [5]:

ticker = tickers[1] # change tickers by changing the index
print(f"{ticker} {watch.data[ticker].shape}\nColumns: {', '.join(list(watch.data[ticker].columns))}")

QQQ (5768, 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 = "5y"
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
2017-02-07	121.609821	122.053483	121.494090	121.802719	17541100	0.0	121.014753	119.871375	116.896796	110.775148	17233090.0
2017-02-08	121.638777	122.178877	121.407299	122.005272	12569100	0.0	121.117952	120.059447	117.052081	110.863883	17052715.0
2017-02-09	122.130652	122.718979	122.063140	122.448929	17542700	0.0	121.252978	120.253788	117.221432	110.957372	16895550.0
2017-02-10	122.718971	123.027601	122.506787	122.853996	13915700	0.0	121.405363	120.477544	117.390999	111.057047	16610210.0
2017-02-13	123.230132	123.702720	123.181913	123.548416	19120200	0.0	121.721710	120.715768	117.602149	111.166413	16719680.0
...	...	...	...	...	...	...	...	...	...	...	...
2022-02-01	364.429993	366.190002	359.140015	365.519989	74433000	0.0	354.433997	368.523997	383.935446	365.349531	94048835.0
2022-02-02	369.760010	370.100006	364.290009	368.489990	78777100	0.0	354.634995	367.124997	383.235753	365.515881	95086330.0
2022-02-03	358.529999	361.929993	352.459991	353.549988	95384800	0.0	353.817993	365.587996	382.330939	365.593182	96068580.0
2022-02-04	354.079987	361.399994	351.970001	358.010010	86127500	0.0	354.449994	364.287497	381.551680	365.713161	96834240.0
2022-02-07	358.619995	361.040009	354.950012	358.165009	42674328	0.0	354.936496	363.202748	380.749953	365.812890	95337256.4

1260 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, 20) > ta.sma(asset.close, 50) # SMA(20) > SMA(50)
# long = ta.ema(asset.close, 8) > ta.ema(asset.close, 21) # EMA(8) > EMA(21)
# long = ta.increasing(ta.ema(asset.close, 20))
# long = ta.macd(asset.close).iloc[:,1] > 0 # MACD Histogram is positive

# 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, cumulative=False)
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
2022-02-01	0.0	354.433997	368.523997	383.935446	365.349531	94048835.0	357.733558	366.959410	376.636208	0.006803
2022-02-02	0.0	354.634995	367.124997	383.235753	365.515881	95086330.0	360.123876	367.098553	376.316749	0.008125
2022-02-03	0.0	353.817993	365.587996	382.330939	365.593182	96068580.0	358.663012	365.866866	375.423935	-0.040544
2022-02-04	0.0	354.449994	364.287497	381.551680	365.713161	96834240.0	358.517901	365.152606	374.741036	0.012615
2022-02-07	0.0	354.936496	363.202748	380.749953	365.812890	95337256.4	358.439480	364.517370	374.090995	0.000433

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
2022-02-01	0	0	0	0
2022-02-02	0	0	0	0
2022-02-03	0	0	0	0
2022-02-04	0	0	0	0
2022-02-07	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}%")

tradelist = trades
if rt_trades > 10:
    tradelist = trades.tail(10)
tradelist

Out [9]:

Trades Total | Round Trip:	22 | 11
Trade Coverage: 74.29%

	Signal	Entry	Exit
Date
2020-04-27	1	213.766617	NaN
2020-10-01	-1	NaN	280.388519
2020-10-16	1	286.607269	NaN
2021-03-11	-1	NaN	316.515167
2021-04-13	1	339.395111	NaN
2021-05-26	-1	NaN	332.947998
2021-06-16	1	339.803650	NaN
2021-10-05	-1	NaN	356.924133
2021-11-02	1	388.553711	NaN
2022-01-07	-1	NaN	379.859985

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':'\nQQQ [D for 5y(1260 bars)] from Tuesday 2-7-2017 to Monday 2-7-2022\nLast OHLCV: (358.6200, 361.0400, 354.9500, 358.1650, 42674328)\nMonday February 7, 2022, NYSE: 7:58:00, Local: 11:58:00 PST, Day 38/365 (10.00%)'}, 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 0x1301d8c10>

Buy and Hold Returns (PCTRET_1)

In [15]:

((asset.PCTRET_1 + 1).cumprod() - 1).plot(figsize=(16, 3), kind="area", stacked=False, color=colors("GyOr"), title="B&H Percent Returns", alpha=0.4, grid=True).axhline(0, color="black")

Out [15]:

<matplotlib.lines.Line2D at 0x1301e6430>

Cum. Active Returns (ACTRET_1)

In [16]:

((asset.ACTRET_1 + 1).cumprod() - 1).plot(figsize=(16, 3), kind="area", stacked=False, color=colors("GyOr")[-1], title="B&H Cum. Active Returns", alpha=0.4, grid=True).axhline(0, color="black")

Out [16]:

<matplotlib.lines.Line2D at 0x1302d8a00>

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.

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