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 v0.3.54b0
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(7328, 7): 3235.1207 ms (3.2351 s)
[+] Saving: /Users/kj/av_data/SPY_D.csv
[i] Analysis Time: 33.3046 ms (0.0333 s) for 5 columns (avg 6.6620 ms / col).
[+] Downloading[yahoo]: QQQ[D]
[+] yf | QQQ(5786, 7): 3151.7639 ms (3.1518 s)
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Analysis Time: 3.4324 ms (0.0034 s) for 5 columns (avg 0.6871 ms / col).
[+] Downloading[yahoo]: AAPL[D]
[+] yf | AAPL(10395, 7): 2917.6695 ms (2.9177 s)
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Analysis Time: 4.1406 ms (0.0041 s) for 5 columns (avg 0.8293 ms / col).
[+] Downloading[yahoo]: TSLA[D]
[+] yf | TSLA(2942, 7): 2911.5245 ms (2.9115 s)
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Analysis Time: 2.8767 ms (0.0029 s) for 5 columns (avg 0.5759 ms / col).
[+] Downloading[yahoo]: BTC-USD[D]
[+] yf | BTC-USD(2726, 7): 2840.3963 ms (2.8404 s)
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Analysis Time: 4.2543 ms (0.0043 s) for 5 columns (avg 0.8517 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 (5786, 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
2017-03-06	125.949921	126.297130	125.583433	126.084946	12026200	0.0	125.923898	124.588588	120.784825	112.861735	16201560.0
2017-03-07	125.834208	126.422534	125.650956	125.872780	15500900	0.0	125.929687	124.814274	120.978683	112.987363	16395100.0
2017-03-08	125.949941	126.490041	125.824570	126.094620	15776000	0.0	125.952836	125.028869	121.183537	113.108551	16306845.0
2017-03-09	126.065715	126.364698	125.515961	126.191086	20855300	0.0	126.032890	125.238160	121.388776	113.230891	16721155.0
2017-03-10	126.769746	126.924064	126.171780	126.721519	21922600	0.0	126.141877	125.451790	121.592664	113.345694	16940150.0
...	...	...	...	...	...	...	...	...	...	...	...
2022-02-28	342.510010	348.540009	341.320007	346.799988	77226200	0.0	344.717999	352.113498	368.444710	367.035715	80833990.0
2022-03-01	345.750000	348.079987	338.899994	341.489990	67407000	0.0	344.117999	351.035498	367.547579	367.116988	79488020.0
2022-03-02	343.079987	348.589996	340.239990	347.220001	70609000	0.0	343.226999	350.120499	366.803599	367.236776	79296820.0
2022-03-03	349.929993	350.040009	340.350006	342.260010	69815200	0.0	341.848999	348.809000	366.034999	367.342676	78848725.0
2022-03-04	339.839996	341.309998	335.140015	335.320007	29548226	0.0	340.835999	347.897501	364.957199	367.412032	75556896.3

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
2022-02-28	0.0	344.717999	352.113498	368.444710	367.035715	80833990.0	344.307560	350.872017	362.574876	0.002979
2022-03-01	0.0	344.117999	351.035498	367.547579	367.116988	79488020.0	343.681434	350.019105	361.748018	-0.015311
2022-03-02	0.0	343.226999	350.120499	366.803599	367.236776	79296820.0	344.467782	349.764641	361.178292	0.016779
2022-03-03	0.0	341.848999	348.809000	366.034999	367.342676	78848725.0	343.977166	349.082402	360.436398	-0.014285
2022-03-04	0.0	340.835999	347.897501	364.957199	367.412032	75556896.3	342.053353	347.831275	359.451442	-0.020277

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
2022-02-28	0	0	0	0
2022-03-01	0	0	0	0
2022-03-02	0	0	0	0
2022-03-03	0	0	0	0
2022-03-04	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: 72.86%

	Signal	Entry	Exit
Date
2020-04-27	1	213.766632	NaN
2020-10-01	-1	NaN	280.388519
2020-10-16	1	286.607269	NaN
2021-03-11	-1	NaN	316.515198
2021-04-13	1	339.395142	NaN
2021-05-26	-1	NaN	332.947998
2021-06-16	1	339.803680	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: (339.8400, 341.3100, 335.1400, 335.3200, 29548226), Change (%): 4.5200 (1.3300 %)\nFriday March 4, 2022, NYSE: 4:12:24, Local: 8:12:24 PST, Day 63/365 (17.00%)'}, 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=["limegreen"], alpha=0.65) # Green Area
short_trend.plot(figsize=(16, 0.85), kind="area", stacked=True, color=["orangered"], alpha=0.65) # 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=["gray", "limegreen"], alpha=1, grid=True).axhline(0, color="black")

Out [14]:

<matplotlib.lines.Line2D at 0x10e668ee0>

Buy and Hold Returns (PCTRET_1)

In [15]:

((asset.PCTRET_1 + 1).cumprod() - 1).plot(figsize=(16, 3), kind="area", stacked=False, color=["limegreen"], title="B&H Percent Returns", alpha=0.9, grid=True).axhline(0, color="black")

Out [15]:

<matplotlib.lines.Line2D at 0x132503fd0>

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")
((asset.ACTRET_1 + 1).cumprod() - 1).plot(figsize=(16, 3), kind="area", stacked=False, color=["limegreen"], title="B&H Cum. Active Returns", alpha=0.65, grid=True).axhline(0, color="black")

Out [16]:

<matplotlib.lines.Line2D at 0x1325a1670>

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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