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 v0.3.63b0
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(7367, 7): 3196.9914 ms (3.1970 s)
[+] Saving: /Users/kj/av_data/SPY_D.csv
[i] Analysis Time: 34.3883 ms (0.0344 s) for 5 columns (avg 6.8791 ms / col)
[+] Downloading[yahoo]: QQQ[D]
[+] yf | QQQ(5825, 7): 4333.8306 ms (4.3338 s)
[+] Saving: /Users/kj/av_data/QQQ_D.csv
[i] Analysis Time: 3.3304 ms (0.0033 s) for 5 columns (avg 0.6667 ms / col)
[+] Downloading[yahoo]: AAPL[D]
[+] yf | AAPL(10434, 7): 4274.9140 ms (4.2749 s)
[+] Saving: /Users/kj/av_data/AAPL_D.csv
[i] Analysis Time: 3.6997 ms (0.0037 s) for 5 columns (avg 0.7406 ms / col)
[+] Downloading[yahoo]: TSLA[D]
[+] yf | TSLA(2981, 7): 3129.0620 ms (3.1291 s)
[+] Saving: /Users/kj/av_data/TSLA_D.csv
[i] Analysis Time: 3.0117 ms (0.0030 s) for 5 columns (avg 0.6030 ms / col)
[+] Downloading[yahoo]: BTC-USD[D]
[+] yf | BTC-USD(2784, 7): 3533.1249 ms (3.5331 s)
[+] Saving: /Users/kj/av_data/BTC-USD_D.csv
[i] Analysis Time: 3.6089 ms (0.0036 s) for 5 columns (avg 0.7226 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 (5825, 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
2017-05-01	131.702961	132.610327	131.645046	132.436569	24815500	0.0	129.452889	128.293103	127.098378	117.352839	20653180.0
2017-05-02	132.600685	132.716529	132.262846	132.658600	18345100	0.0	130.046537	128.540698	127.250689	117.480589	20385420.0
2017-05-03	132.359333	132.407600	131.876692	132.233856	23827600	0.0	130.577444	128.756921	127.382175	117.602671	20906800.0
2017-05-04	132.214573	132.658594	131.818804	132.282135	14630100	0.0	131.006993	129.001620	127.513663	117.726958	20037745.0
2017-05-05	132.619938	132.764725	132.127651	132.764725	19330700	0.0	131.487700	129.267070	127.664244	117.847383	20062670.0
...	...	...	...	...	...	...	...	...	...	...	...
2022-04-25	323.730011	329.899994	322.429993	329.579987	101755900	0.0	338.041000	349.745001	344.924885	367.586838	67700730.0
2022-04-26	327.470001	327.660004	316.859985	317.140015	105819600	0.0	335.666000	347.356502	344.335056	367.366986	70150015.0
2022-04-27	317.239990	322.880005	315.000000	316.760010	111204200	0.0	333.397000	344.635002	343.729037	367.145234	72306575.0
2022-04-28	321.850006	329.890015	317.519989	328.010010	99450000	0.0	331.563000	342.681003	343.175432	366.976494	73841190.0
2022-04-29	323.700012	327.230011	312.600006	313.250000	91856700	0.0	329.045001	340.216502	342.328425	366.746658	75083455.0

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
2022-04-25	0.0	338.041000	349.745001	344.924885	367.586838	67700730.0	336.456901	343.546748	348.865113	0.012846
2022-04-26	0.0	335.666000	347.356502	344.335056	367.366986	70150015.0	332.164260	341.146136	347.620992	-0.037745
2022-04-27	0.0	333.397000	344.635002	343.729037	367.145234	72306575.0	328.741093	338.929215	346.410757	-0.001198
2022-04-28	0.0	331.563000	342.681003	343.175432	366.976494	73841190.0	328.578630	337.936560	345.689159	0.035516
2022-04-29	0.0	329.045001	340.216502	342.328425	366.746658	75083455.0	325.172268	335.692328	344.417035	-0.044999

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]:

tsignals

	TS_Trends	TS_Trades	TS_Entries	TS_Exits
Date
2022-04-25	1	0	0	0
2022-04-26	1	0	0	0
2022-04-27	1	0	0	0
2022-04-28	0	-1	0	1
2022-04-29	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: 71.19%

	Signal	Entry	Exit
Date
2020-10-16	1	286.253357	NaN
2021-03-11	-1	NaN	316.124359
2021-04-13	1	338.976044	NaN
2021-05-26	-1	NaN	332.536896
2021-06-16	1	339.384125	NaN
2021-10-05	-1	NaN	356.483398
2021-11-02	1	388.073944	NaN
2022-01-07	-1	NaN	379.390961
2022-04-05	1	361.100006	NaN
2022-04-28	-1	NaN	328.010010

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: (323.7000, 327.2300, 312.6000, 313.2500, 91856700), Change (%): 10.4500 (3.2283 %)\nSunday May 1, 2022, NYSE: 14:15:43, Local: 18:15:43 PDT, Day 121/365 (33.00%)'}, 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 0x132855d00>

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 0x13206da60>

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 0x133835ee0>

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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