TA Analysis with Pandas TA

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 v0.3.48b0
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
[i] Loaded SPY[D]: SPY_D.csv
[i] Analysis Time: 32.4298 ms (0.0324 s) for 5 columns (avg 6.4872 ms / col).
[i] Loaded QQQ[D]: QQQ_D.csv
[i] Analysis Time: 2.6830 ms (0.0027 s) for 5 columns (avg 0.5372 ms / col).
[i] Loaded AAPL[D]: AAPL_D.csv
[i] Analysis Time: 2.8270 ms (0.0028 s) for 5 columns (avg 0.5660 ms / col).
[i] Loaded TSLA[D]: TSLA_D.csv
[i] Analysis Time: 2.4430 ms (0.0024 s) for 5 columns (avg 0.4892 ms / col).
[i] Loaded BTC-USD[D]: BTC-USD_D.csv
[i] Analysis Time: 2.4138 ms (0.0024 s) for 5 columns (avg 0.4833 ms / col).

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 (5782, 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
2017-02-28	125.757033	125.814899	125.052975	125.400185	16598600	0.0	125.240086	123.480899	119.942171	112.361493	16326435.00
2017-03-01	126.200704	127.001200	125.959587	126.769737	25813800	0.0	125.520745	123.812194	120.161973	112.490696	16662570.00
2017-03-02	126.740806	126.760085	125.978868	126.133186	19951400	0.0	125.664453	124.070189	120.377628	112.610403	16517045.00
2017-03-03	126.046393	126.403242	125.708824	126.364670	13722700	0.0	125.835163	124.345063	120.588461	112.737677	16448110.00
2017-03-06	125.949898	126.297107	125.583410	126.084923	12026200	0.0	125.923891	124.588589	120.784826	112.861734	16201560.00
...	...	...	...	...	...	...	...	...	...	...	...
2022-02-22	338.489990	344.040009	334.350006	338.079987	85967100	0.0	351.604001	353.118498	372.665317	366.637045	85912250.00
2022-02-23	341.320007	342.179993	329.100006	329.420013	86215400	0.0	348.634003	352.334000	371.303671	366.661107	84003100.00
2022-02-24	318.839996	341.040009	318.260010	340.489990	130614100	0.0	346.010001	352.129999	370.278278	366.742762	83205830.00
2022-02-25	341.309998	345.980011	337.390015	345.769989	78776600	0.0	344.744000	352.363498	369.439581	366.892766	82381875.00
2022-02-28	342.510010	348.540009	342.149994	344.209991	48205859	0.0	344.459000	351.983998	368.392910	367.022765	79383772.95

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
2022-02-22	0.0	351.604001	353.118498	372.665317	366.637045	85912250.00	347.759289	355.318573	366.335378	-0.010044
2022-02-23	0.0	348.634003	352.334000	371.303671	366.661107	84003100.00	343.683894	352.964158	364.887717	-0.025615
2022-02-24	0.0	346.010001	352.129999	370.278278	366.742762	83205830.00	342.974138	351.830143	363.930943	0.033604
2022-02-25	0.0	344.744000	352.363498	369.439581	366.892766	82381875.00	343.595438	351.279220	363.218749	0.015507
2022-02-28	0.0	344.459000	351.983998	368.392910	367.022765	79383772.95	343.732005	350.636563	362.473308	-0.004512

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
2022-02-22	0	0	0	0
2022-02-23	0	0	0	0
2022-02-24	0	0	0	0
2022-02-25	0	0	0	0
2022-02-28	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: 73.17%

	Signal	Entry	Exit
Date
2020-04-27	1	213.766617	NaN
2020-10-01	-1	NaN	280.388550
2020-10-16	1	286.607239	NaN
2021-03-11	-1	NaN	316.515167
2021-04-13	1	339.395142	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]
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)})"
_oc_change = asset.iloc[-1].open - asset.iloc[-1].close
_oc_change_pct = _oc_change / asset.iloc[-1].open
oc_change = f"{_oc_change:.4f} ({100 * _oc_change_pct:.4f} %)"
ptitle = f"\n{ticker} [{tf} for {duration}({recent} bars)]\n{last_ohlcv}, Change (%): {oc_change}\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)]\nLast OHLCV: (342.5100, 348.5400, 342.1500, 344.2100, 48205859), Change (%): -1.7000 (-0.4963 %)\nMonday February 28, 2022, NYSE: 7:09:11, Local: 11:09:11 PST, Day 59/365 (16.00%)'}, 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 0x1316d0ee0>

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 0x13205efd0>

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 0x131f41820>

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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