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
[i] Loaded SPY[D]: SPY_D.csv
[i] Analysis Time: 34.7742 ms (0.0348 s) for 5 columns (avg 6.9562 ms / col)
[i] Loaded QQQ[D]: QQQ_D.csv
[i] Analysis Time: 3.1180 ms (0.0031 s) for 5 columns (avg 0.6242 ms / col)
[i] Loaded AAPL[D]: AAPL_D.csv
[i] Analysis Time: 3.1966 ms (0.0032 s) for 5 columns (avg 0.6401 ms / col)
[i] Loaded TSLA[D]: TSLA_D.csv
[i] Analysis Time: 2.7987 ms (0.0028 s) for 5 columns (avg 0.5605 ms / col)
[i] Loaded BTC-USD[D]: BTC-USD_D.csv
[i] Analysis Time: 2.8410 ms (0.0028 s) for 5 columns (avg 0.5692 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 (5801, 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
2017-03-27	125.196966	126.519411	124.907379	126.297386	18472400	0.0	126.491342	126.296040	123.630030	114.376488	19309820.00
2017-03-28	126.297405	127.446093	126.017480	127.069633	23721900	0.0	126.551437	126.387253	123.798677	114.491380	19665985.00
2017-03-29	127.156459	127.706676	127.021320	127.658409	13710000	0.0	126.592381	126.439514	123.986228	114.613563	19060795.00
2017-03-30	127.610166	128.005935	127.494337	127.870796	16092400	0.0	126.663233	126.534182	124.173210	114.736808	18867845.00
2017-03-31	127.687432	128.131468	127.542646	127.783966	19841400	0.0	126.729840	126.612949	124.359612	114.861100	19173780.00
...	...	...	...	...	...	...	...	...	...	...	...
2022-03-21	350.200012	352.480011	345.579987	350.079987	73799100	0.0	334.295990	336.622971	350.758813	367.121709	81985850.00
2022-03-22	350.589996	357.850006	350.200012	356.959991	63345900	0.0	337.696915	337.587843	350.310193	367.231533	80854790.00
2022-03-23	354.010010	357.660004	351.769989	351.829987	70615500	0.0	339.422278	338.728680	349.753981	367.314911	80074795.00
2022-03-24	353.799988	359.700012	351.589996	359.649994	53383700	0.0	342.301181	339.707703	349.240109	367.436991	76213275.00
2022-03-25	359.589996	360.660004	354.943787	355.645813	24863691	0.0	345.465820	340.222841	348.615591	367.521584	73514354.55

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
2022-03-21	0.0	334.295990	336.622971	350.758813	367.121709	81985850.00	340.435699	340.087312	350.327677	-0.002780
2022-03-22	0.0	337.696915	337.587843	350.310193	367.231533	80854790.00	344.107764	341.621192	350.587768	0.019653
2022-03-23	0.0	339.422278	338.728680	349.753981	367.314911	80074795.00	345.823813	342.549265	350.636483	-0.014371
2022-03-24	0.0	342.301181	339.707703	349.240109	367.436991	76213275.00	348.896298	344.103876	350.989954	0.022227
2022-03-25	0.0	345.465820	340.222841	348.615591	367.521584	73514354.55	350.396190	345.153143	351.172536	-0.011134

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
2022-03-21	0	0	0	0
2022-03-22	0	0	0	0
2022-03-23	0	0	0	0
2022-03-24	0	0	0	0
2022-03-25	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: 71.67%

	Signal	Entry	Exit
Date
2020-04-27	1	213.502670	NaN
2020-10-01	-1	NaN	280.042267
2020-10-16	1	286.253387	NaN
2021-03-11	-1	NaN	316.124329
2021-04-13	1	338.976044	NaN
2021-05-26	-1	NaN	332.536896
2021-06-16	1	339.384094	NaN
2021-10-05	-1	NaN	356.483429
2021-11-02	1	388.073944	NaN
2022-01-07	-1	NaN	379.390961

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: (359.5900, 360.6600, 354.9438, 355.6458, 24863691), Change (%): 3.9442 (1.0969 %)\nSunday March 27, 2022, NYSE: 12:50:00, Local: 16:50:00 PDT, Day 86/365 (24.00%)'}, 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 0x13037ff10>

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 0x130402430>

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 0x130470400>

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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