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Basics of Stock Backtesting in Python

Stock Backtesting in Python is way of testing our strategy in a historical data to see if our strategy makes any money or not. Let's start with a simple story.

John and Joe are two best friends. They both earned some money from their hard working corporate job and wanted to invest it in a stock market. Unlike Joe, John is clever and does not fall for any influence of stock's price increasing and decreasing. They studied some Statistics and Probability along with Economics in college and and they love their money. Joe followed trend and bought some stock of X and felt glad that his stock's price increased by some % in few days. John was calm person and thought that John's stock position is increased but he is not earning any money and only way to earn is by selling it. John wanted to get back in time and questioned himself what will happen if I try to buy some stock of X and sell it if price increased by 10% or decrease by 5%. Then I will buy as much stock as possible from the amount I have. How much would have I earned today? Well he did not know but what he tried to do is a simple stock backtesting example.

Here in this blog, we will start with our very simple strategy and then try to use of the most popular stock backtesting Python package Backtesting.py. But first, let's install it.

!pip install backtesting
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Before going into stock backtesting, lets choose the data of any stock. We will choose data of AAPL from yfinance. If it is not installed, we can do so by pip install yfinance.

!pip install yfinance --user
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import pandas as pd
import yfinance as yf

Next is to download data. We can download data as following.

data = yf.download("AAPL", start="2015-01-01", end="2022-04-30")
del data['Adj Close']
del data['Volume']
data
[*********************100%***********************]  1 of 1 completed
Open High Low Close
Date
2015-01-02 27.847500 27.860001 26.837500 27.332500
2015-01-05 27.072500 27.162500 26.352501 26.562500
2015-01-06 26.635000 26.857500 26.157499 26.565001
2015-01-07 26.799999 27.049999 26.674999 26.937500
2015-01-08 27.307501 28.037500 27.174999 27.972500
... ... ... ... ...
2022-04-25 161.119995 163.169998 158.460007 162.880005
2022-04-26 162.250000 162.339996 156.720001 156.800003
2022-04-27 155.910004 159.789993 155.380005 156.570007
2022-04-28 159.250000 164.520004 158.929993 163.639999
2022-04-29 161.839996 166.199997 157.250000 157.649994

1845 rows × 4 columns

Our data will be daily floorsheet data and we will make strategy on it. Alternatively we could get data for testing from backtesting.py too but it only allows GOOG.

import backtesting.test as btest
btest.GOOG
Open High Low Close Volume
2004-08-19 100.00 104.06 95.96 100.34 22351900
2004-08-20 101.01 109.08 100.50 108.31 11428600
2004-08-23 110.75 113.48 109.05 109.40 9137200
2004-08-24 111.24 111.60 103.57 104.87 7631300
2004-08-25 104.96 108.00 103.88 106.00 4598900
... ... ... ... ... ...
2013-02-25 802.30 808.41 790.49 790.77 2303900
2013-02-26 795.00 795.95 784.40 790.13 2202500
2013-02-27 794.80 804.75 791.11 799.78 2026100
2013-02-28 801.10 806.99 801.03 801.20 2265800
2013-03-01 797.80 807.14 796.15 806.19 2175400

2148 rows × 5 columns

Preparing SMA

We will work on our data from yfinance next. There is a good availability of classes and modules in backtesting and lets use them instead of writing our own indicators. But I have written many indicators from scratch and you can find them here. Here, SMA stands for Simple Moving Average.

We start by making a class that inherits Strategy class inside backtesting and we do not need anything at all at this time but lets use crossover and SMA too. But this will be covered later. First lets take a look into our data and try to plot SMA of two periods, one longer and one shorter. One SMA of 20 days and another of 40 days. Our simple strategy will be to buy when small SMA crosses over bigger SMA.

n1,n2=20,40

ndata = data.copy()

ndata[f'SMA_{n1}'] = ndata.Close.rolling(n1).mean()
ndata[f'SMA_{n2}'] = ndata.Close.rolling(n2).mean()

ndata[[f'SMA_{n1}', f'SMA_{n2}']].plot(figsize=(15,10))

png

We can see that SMA_20 and SMA_40 are crossing over each other in multiple times. But the plot looks little huge so lets take data of last 200 days only.

last = 200
n1,n2=20,40

tdata = data.copy().tail(last)

tdata[f'SMA_{n1}'] = tdata.Close.rolling(n1).mean()
tdata[f'SMA_{n2}'] = tdata.Close.rolling(n2).mean()

tdata[[f'SMA_{n1}', f'SMA_{n2}']].plot(figsize=(15,10))

png

Our Simple Strategy

Now let's make our stock backtesting strategy. If the short SMA crosses over large SMA, we buy and hold positions because we saw that it has increased the value of price recently and could increase in future too. But if short SMA crosses below large SMA, we sell our holding positions because there has been recent price drops. In above example we will do trades whenever crossover happens. A simple way to find a crossover is by comparing difference between current price and previous. If the difference was positive in past and negative now then we do trade and vice versa. Note that we buy on the Open price of next day.

tdata['sma1_gt_sma2'] = tdata[f'SMA_{n1}']>tdata[f'SMA_{n2}']
tdata['crossed'] = (tdata.sma1_gt_sma2!=tdata.sma1_gt_sma2.shift(1))
print(f"Num Corssed: {tdata.crossed.sum()-1}")
tdata
Num Corssed: 7
Open High Low Close SMA_20 SMA_40 sma1_gt_sma2 crossed
Date
2021-07-16 148.460007 149.759995 145.880005 146.389999 NaN NaN False True
2021-07-19 143.750000 144.070007 141.669998 142.449997 NaN NaN False False
2021-07-20 143.460007 147.100006 142.960007 146.149994 NaN NaN False False
2021-07-21 145.529999 146.130005 144.630005 145.399994 NaN NaN False False
2021-07-22 145.940002 148.199997 145.809998 146.800003 NaN NaN False False
... ... ... ... ... ... ... ... ...
2022-04-25 161.119995 163.169998 158.460007 162.880005 170.435000 166.72550 True False
2022-04-26 162.250000 162.339996 156.720001 156.800003 169.495000 166.51750 True False
2022-04-27 155.910004 159.789993 155.380005 156.570007 168.375500 166.35175 True False
2022-04-28 159.250000 164.520004 158.929993 163.639999 167.668999 166.27875 True False
2022-04-29 161.839996 166.199997 157.250000 157.649994 166.820999 166.06425 True False

200 rows × 8 columns

In above code, we made new column where we checked if SMA1 is higher than SMA2 or not and in next crossed column we checked if the status of SMA1>SMA2 still holds same from the previous time. And when it is false, we do trade. We should ignore the first one because it will give us NaN value on shift. Let's assume that we have USD 10000 in cash and want to do trade. Since we have SMA1>SMA2 column, we buy only when there is crossed True and sma1_gt_sma2 True as well. And we sell only when there is crossed True and sma1_gt_sma2 is False.

Trading Result

To find stock backtesting trades data, we loop through the data and if yesterday's SMA1>SMA2 then we buy on today's Open price and selling happens on same way.

  • If crossed and SMA1>SMA2: buy positions based on remaining amount and add positions.
  • If crossed and SMA1<SMA2: sell available positions and add remaining amount.
  • On last day sell all positions and add remaining amount.
ntdata = tdata.reset_index().copy()
ntdata['crossed']=ntdata.crossed.shift(1)
ntdata['sma1_gt_sma2']=ntdata.sma1_gt_sma2.shift(1)

positions = 0
rem_amt=10000
lr = len(ntdata)-1
trades = []
tinfo=[]

for i, row in ntdata.iterrows():
    if i!=0:
        if row.crossed and row.sma1_gt_sma2:
            positions=int(rem_amt/row.Open)
            rem_amt= rem_amt-row.Open*positions
            tinfo.append(positions)
            tinfo.append(row.Open)
            tinfo.append(row.Date)

        if row.crossed==True and row.sma1_gt_sma2==False and positions>0:
            rem_amt = rem_amt+row.Open*positions
            tinfo.append(row.Date)
            tinfo.append(row.Open)
            trades.append(tinfo)
            tinfo=[]
            positions = 0

    if i==lr and positions>0:
        rem_amt=rem_amt + positions*row.Open

        tinfo.append(row.Date)
        tinfo.append(row.Open)
        trades.append(tinfo)

        positions = 0
        ntdata.loc[i, 'positions'] = positions
        ntdata.loc[i, 'rem_amount'] = rem_amt

trades = pd.DataFrame(trades, columns=['Positions', 'Buy', 'Entry', 'Exit', 'Sell'])
trades['return']=((trades['Sell']-trades['Buy'])*trades.Positions).cumsum()

trades
Positions Buy Entry Exit Sell return
0 66 150.630005 2021-09-13 2021-10-01 141.899994 -576.180725
1 62 150.389999 2021-11-03 2022-01-27 162.449997 171.539124
2 61 164.699997 2022-03-01 2022-03-02 164.389999 152.629272
3 58 172.360001 2022-04-06 2022-04-29 161.839996 -457.530975

Looking over the above table, in return column, we are in 457 loss overall. What if we did this testing with larger period of data?

Our Strategy in a Larger Period

Lets start from the last 1000 day and forth.

n1,n2=20,40
last = 1000

tdata = data.copy().tail(last)

tdata[f'SMA_{n1}'] = tdata.Close.rolling(n1).mean()
tdata[f'SMA_{n2}'] = tdata.Close.rolling(n2).mean()

tdata['sma1_gt_sma2'] = tdata[f'SMA_{n1}']>tdata[f'SMA_{n2}']
tdata['crossed'] = (tdata.sma1_gt_sma2!=tdata.sma1_gt_sma2.shift(1))
print(f"Num Corssed: {tdata.crossed.sum()-1}")

ntdata = tdata.reset_index().copy()
ntdata['crossed']=ntdata.crossed.shift(1)
ntdata['sma1_gt_sma2']=ntdata.sma1_gt_sma2.shift(1)

positions = 0
rem_amt=10000
lr = len(ntdata)-1
trades = []
tinfo=[]

for i, row in ntdata.iterrows():
    if i!=0:
        if row.crossed and row.sma1_gt_sma2:
            positions=int(rem_amt/row.Open)
            rem_amt= rem_amt-row.Open*positions
            tinfo.append(positions)
            tinfo.append(row.Open)
            tinfo.append(row.Date)

        if row.crossed==True and row.sma1_gt_sma2==False and positions>0:
            rem_amt = rem_amt+row.Open*positions
            tinfo.append(row.Date)
            tinfo.append(row.Open)
            trades.append(tinfo)
            tinfo=[]
            positions = 0

    if i==lr and positions>0:
        rem_amt=rem_amt + positions*row.Open

        tinfo.append(row.Date)
        tinfo.append(row.Open)
        trades.append(tinfo)

        positions = 0
        ntdata.loc[i, 'positions'] = positions
        ntdata.loc[i, 'rem_amount'] = rem_amt

trades = pd.DataFrame(trades, columns=['Positions', 'Buy', 'Entry', 'Exit', 'Sell'])
trades['return']=((trades['Sell']-trades['Buy'])*trades.Positions).cumsum()

trades
Num Corssed: 23
Positions Buy Entry Exit Sell return
0 205 48.652500 2018-07-26 2018-10-26 53.974998 1091.112156
1 257 43.099998 2019-02-07 2019-05-23 44.950001 1566.562744
2 232 49.669998 2019-06-28 2019-08-28 51.025002 1880.923523
3 228 52.097500 2019-09-04 2020-03-02 70.570000 6092.653488
4 232 69.300003 2020-04-24 2020-09-29 114.550003 16590.653488
5 233 114.010002 2020-10-26 2020-11-18 118.610001 17662.453133
6 228 121.010002 2020-12-01 2021-02-26 122.589996 18022.691811
7 207 134.940002 2021-04-14 2021-05-24 126.010002 16174.181747
8 194 134.449997 2021-06-24 2021-10-01 141.899994 17619.481155
9 183 150.389999 2021-11-03 2022-01-27 162.449997 19826.460709
10 181 164.699997 2022-03-01 2022-03-02 164.389999 19770.351151
11 172 172.360001 2022-04-06 2022-04-29 161.839996 17960.910416

It looks like we actually made some money while testing on larger period.

Strategy with Backtesting

Until now we designed a very simple strategy and did trading and to do so, we had to write too many codes but why do we need to struggle that hard while there is already one open source package available which handles our struggles? Following is a modified version of our strategy and it is modified from the Quick Start page.

from backtesting import Strategy
from backtesting.lib import crossover
from backtesting.test import SMA

class SmaCross(Strategy):
    # Define the two MA lags as *class variables*
    # for later optimization
    n1 = 20
    n2 = 40

    def init(self):
        # Precompute the two moving averages
        self.sma1 = self.I(SMA, self.data.Close, self.n1)
        self.sma2 = self.I(SMA, self.data.Close, self.n2)

    def next(self):
        # If sma1 crosses above sma2, close any existing
        # short trades, and buy the asset
        if crossover(self.sma1, self.sma2):
            self.position.close()
            self.buy()

        # Else, if sma1 crosses below sma2, close any existing
        # long trades, and sell the asset
        elif crossover(self.sma2, self.sma1):
            self.position.close()
            self.sell()
from backtesting import Backtest

bt = Backtest(data.tail(last), SmaCross, cash=10000, commission=0)
stats = bt.run()
stats
Start                     2018-05-11 00:00:00
End                       2022-04-29 00:00:00
Duration                   1449 days 00:00:00
Exposure Time [%]                        94.8
Equity Final [$]                 21836.199413
Equity Peak [$]                  34464.403912
Return [%]                         118.361994
Buy & Hold Return [%]              234.376153
Return (Ann.) [%]                   21.751022
Volatility (Ann.) [%]               38.394298
Sharpe Ratio                         0.566517
Sortino Ratio                        1.031964
Calmar Ratio                         0.562183
Max. Drawdown [%]                  -38.690305
Avg. Drawdown [%]                   -5.507696
Max. Drawdown Duration      458 days 00:00:00
Avg. Drawdown Duration       34 days 00:00:00
# Trades                                   23
Win Rate [%]                        52.173913
Best Trade [%]                      65.295812
Worst Trade [%]                     -10.50055
Avg. Trade [%]                       3.457111
Max. Trade Duration         180 days 00:00:00
Avg. Trade Duration          60 days 00:00:00
Profit Factor                        2.831255
Expectancy [%]                       4.500417
SQN                                  0.873935
_strategy                            SmaCross
_equity_curve                             ...
_trades                       Size  EntryB...
dtype: object

We start by importing necessary classes and methods. We create a new class for our own strategy which inherits Strategy. We initialize variables and then SMA. When doing run(), the next() method loops through the data rows and perform checks inside it. We can pass commission percent to calculate how much commission do we have to pay to our broker.

Trades

The trades table using backtesting is different than ours.

stats['_trades']  # Contains individual trade data
Size EntryBar ExitBar EntryPrice ExitPrice PnL ReturnPct EntryTime ExitTime Duration
0 205 52 117 48.652500 53.974998 1091.112156 0.109398 2018-07-26 2018-10-26 92 days
1 -205 117 186 53.974998 43.099998 2229.375000 0.201482 2018-10-26 2019-02-07 104 days
2 309 186 259 43.099998 44.950001 571.650707 0.042923 2019-02-07 2019-05-23 105 days
3 -309 259 284 44.950001 49.669998 -1458.479198 -0.105006 2019-05-23 2019-06-28 36 days
4 250 284 326 49.669998 51.025002 338.750839 0.027280 2019-06-28 2019-08-28 61 days
5 -250 326 330 51.025002 52.097500 -268.124580 -0.021019 2019-08-28 2019-09-04 7 days
6 240 330 453 52.097500 70.570000 4433.399963 0.354576 2019-09-04 2020-03-02 180 days
7 -240 453 491 70.570000 69.300003 304.799194 0.017996 2020-03-02 2020-04-24 53 days
8 248 491 600 69.300003 114.550003 11222.000000 0.652958 2020-04-24 2020-09-29 158 days
9 -248 600 619 114.550003 114.010002 133.920227 0.004714 2020-09-29 2020-10-26 27 days
10 250 619 636 114.010002 118.610001 1149.999619 0.040347 2020-10-26 2020-11-18 23 days
11 -250 636 644 118.610001 121.010002 -600.000381 -0.020234 2020-11-18 2020-12-01 13 days
12 240 644 703 121.010002 122.589996 379.198608 0.013057 2020-12-01 2021-02-26 87 days
13 -240 703 735 122.589996 134.940002 -2964.001465 -0.100742 2021-02-26 2021-04-14 47 days
14 196 735 763 134.940002 126.010002 -1750.280060 -0.066178 2021-04-14 2021-05-24 40 days
15 -196 763 785 126.010002 134.449997 -1654.238983 -0.066979 2021-05-24 2021-06-24 31 days
16 172 785 854 134.449997 141.899994 1281.399475 0.055411 2021-06-24 2021-10-01 99 days
17 -172 854 877 141.899994 150.389999 -1460.280945 -0.059831 2021-10-01 2021-11-03 33 days
18 152 877 935 150.389999 162.449997 1833.119629 0.080191 2021-11-03 2022-01-27 85 days
19 -152 935 957 162.449997 164.699997 -342.000000 -0.013850 2022-01-27 2022-03-01 33 days
20 148 957 958 164.699997 164.389999 -45.879639 -0.001882 2022-03-01 2022-03-02 1 days
21 -148 958 983 164.389999 172.360001 -1179.560181 -0.048482 2022-03-02 2022-04-06 35 days
22 134 983 999 172.360001 161.839996 -1409.680573 -0.061035 2022-04-06 2022-04-29 23 days

Plotting

We can even plot our trading with bokeh plot. It is interactive just like plotly

bt.plot()

png png png

Stop Profit and Stop Loss

Profit and stop loss are often used to stay in the safe side. We exit from the trade when there is increase in price and take a profit but reversely, we exit from the trade when there is decrease in price and realize loss.

from backtesting import Strategy
from backtesting.lib import crossover
from backtesting.test import SMA

class SmaCross(Strategy):
    # Define the two MA lags as *class variables*
    # for later optimization
    n1 = 20
    n2 = 40

    def init(self):
        # Precompute the two moving averages
        self.sma1 = self.I(SMA, self.data.Close, self.n1)
        self.sma2 = self.I(SMA, self.data.Close, self.n2)

    def next(self):
        # If sma1 crosses above sma2, close any existing
        # short trades, and buy the asset
        if crossover(self.sma1, self.sma2):
#             self.position.close()
            self.buy(tp=self.data.Close[-1]*1.2, sl=self.data.Close[-1]*0.95)

        # Else, if sma1 crosses below sma2, close any existing
        # long trades, and sell the asset
        elif crossover(self.sma2, self.sma1):
            self.position.close()
#             self.sell()

from backtesting import Backtest

bt = Backtest(data.tail(last), SmaCross, cash=10000, commission=0)
stats = bt.run()
stats         
Start                     2018-05-11 00:00:00
End                       2022-04-29 00:00:00
Duration                   1449 days 00:00:00
Exposure Time [%]                        39.7
Equity Final [$]                 25715.433767
Equity Peak [$]                  26697.815822
Return [%]                         157.154338
Buy & Hold Return [%]              234.376153
Return (Ann.) [%]                   26.872895
Volatility (Ann.) [%]               19.883748
Sharpe Ratio                         1.351501
Sortino Ratio                        2.733988
Calmar Ratio                         2.457561
Max. Drawdown [%]                  -10.934783
Avg. Drawdown [%]                   -2.628023
Max. Drawdown Duration      163 days 00:00:00
Avg. Drawdown Duration       20 days 00:00:00
# Trades                                   12
Win Rate [%]                        66.666667
Best Trade [%]                      21.740146
Worst Trade [%]                     -5.359055
Avg. Trade [%]                        8.20145
Max. Trade Duration          99 days 00:00:00
Avg. Trade Duration          47 days 00:00:00
Profit Factor                        8.893546
Expectancy [%]                       8.683464
SQN                                  2.395319
_strategy                            SmaCross
_equity_curve                             ...
_trades                       Size  EntryB...
dtype: object

In above example, we exit the trade once price increases by 20% or decreases by 5%. Doing so we made some profit as well.

Our Own Strategy in Backtesting

Lets make our own strategy here and implement it on backtesting. I want to do something like below:

  • If EMA 9 > EMA 20 or EMA 50 > EMA 100 then buy.
  • If EMA 9 < EMA 20 or EMA 50 < EMA 100 then close positions.

For calculation of EMA, we can use pandas_ta. We can install it like pip install pandas-ta.

import pandas_ta as ta

from backtesting import Backtest
from backtesting import Strategy
from backtesting.lib import crossover

class EmaCross(Strategy):
    def init(self):
        self.ema9 = self.I(ta.ema, pd.Series(self.data.Close), 9)
        self.ema20 = self.I(ta.ema, pd.Series(self.data.Close), 20)
        self.ema50 = self.I(ta.ema, pd.Series(self.data.Close), 50)
        self.ema100 = self.I(ta.ema, pd.Series(self.data.Close), 100)

    def next(self):
        if crossover(self.ema9, self.ema20) or crossover(self.ema50, self.ema100):
            self.buy()

        elif crossover(self.ema20, self.ema9) or crossover(self.ema100, self.ema50):
            self.position.close()
            # self.sell()

bt = Backtest(data, EmaCross, cash=10000, commission=0.02)
stats = bt.run()
bt.plot()
stats

png png png

Start                     2015-01-02 00:00:00
End                       2022-04-29 00:00:00
Duration                   2674 days 00:00:00
Exposure Time [%]                   63.631436
Equity Final [$]                 24777.296183
Equity Peak [$]                  30443.052752
Return [%]                         147.772962
Buy & Hold Return [%]              476.785846
Return (Ann.) [%]                   13.193633
Volatility (Ann.) [%]                21.81465
Sharpe Ratio                         0.604806
Sortino Ratio                         1.02413
Calmar Ratio                         0.463525
Max. Drawdown [%]                  -28.463721
Avg. Drawdown [%]                   -4.544362
Max. Drawdown Duration      776 days 00:00:00
Avg. Drawdown Duration       53 days 00:00:00
# Trades                                   30
Win Rate [%]                        46.666667
Best Trade [%]                      60.672271
Worst Trade [%]                      -7.85946
Avg. Trade [%]                       3.077886
Max. Trade Duration         190 days 00:00:00
Avg. Trade Duration          56 days 00:00:00
Profit Factor                        2.584764
Expectancy [%]                       3.953845
SQN                                  1.328035
_strategy                            EmaCross
_equity_curve                             ...
_trades                       Size  EntryB...
dtype: object

Looks like we made some money. But this is just another bad strategy we tested.

Testing Percentage Price Oscillator

Following is taken from my another blog.

  • This is a momentum indicator (determines the strength or weakness of a value). But we can view the volatility too.
  • Two EMAs, 26 period and 12 periods are used to calculate PPO.
  • It contains 2 lines, PPO line and signal line. Signal line is an EMA of the 9 Period PPO, so it moves slower than PPO.
  • When PPO line crosses the signal line, it is the time for rise/fall of the price or stock.
  • When PPO line crosses over the signal line from below, then it is a buy signal. Reversely, it is a sell signal when PPO line crosses belo the signal line from above.
  • When PPO line is below the 0, the short term average is below the longer-term average average, which helps indicate a fall of price.
  • Conversely, when PPO line is above 0, the short term average is above the long term average, which helps indicate rise of price.

pandas_ta has PPO too so we do not have to write our own code for it.

ta.ppo(data.Close)
PPO_12_26_9 PPOh_12_26_9 PPOs_12_26_9
2015-01-02 NaN NaN NaN
2015-01-05 NaN NaN NaN
2015-01-06 NaN NaN NaN
2015-01-07 NaN NaN NaN
2015-01-08 NaN NaN NaN
... ... ... ...
2022-04-25 -1.987252 -2.241286 0.254034
2022-04-26 -2.580172 -2.267365 -0.312807
2022-04-27 -3.049812 -2.189604 -0.860208
2022-04-28 -3.039588 -1.743504 -1.296084
2022-04-29 -3.256113 -1.568023 -1.688090

1845 rows × 3 columns

class PPO(Strategy):
    def init(self):
        self.ppo = self.I(ta.ppo, pd.Series(self.data.Close))

    def next(self):
        if crossover(self.ppo[0], self.ppo[2]):
        # if crossover(self.ppo[1], 0):
            # self.position.close()
            self.buy()

        elif crossover(self.ppo[2], self.ppo[0]):
        #if crossover(0,self.ppo[1]):
            self.position.close()
            # self.sell()

bt = Backtest(data, PPO, cash=10000, commission=0.02)
stats = bt.run()
bt.plot()
print(stats)

png png png png

Start                     2015-01-02 00:00:00
End                       2022-04-29 00:00:00
Duration                   2674 days 00:00:00
Exposure Time [%]                    51.00271
Equity Final [$]                 12163.349445
Equity Peak [$]                  14453.298656
Return [%]                          21.633494
Buy & Hold Return [%]              476.785846
Return (Ann.) [%]                    2.711015
Volatility (Ann.) [%]                18.61385
Sharpe Ratio                         0.145645
Sortino Ratio                         0.22052
Calmar Ratio                         0.067051
Max. Drawdown [%]                  -40.432272
Avg. Drawdown [%]                   -7.414292
Max. Drawdown Duration     1942 days 00:00:00
Avg. Drawdown Duration      169 days 00:00:00
# Trades                                   56
Win Rate [%]                        44.642857
Best Trade [%]                      17.890366
Worst Trade [%]                    -15.387924
Avg. Trade [%]                       0.353242
Max. Trade Duration          70 days 00:00:00
Avg. Trade Duration          23 days 00:00:00
Profit Factor                        1.242225
Expectancy [%]                       0.592473
SQN                                  0.433939
_strategy                                 PPO
_equity_curve                             ...
_trades                       Size  EntryB...
dtype: object

Looks like we again made some money. There is not a golden rule that will make a money, its kind of hit and trial.

PPO on BABA


bdata = yf.download("BABA", start="2015-01-01", end="2022-11-30")
del bdata['Adj Close']
del bdata['Volume']

bt = Backtest(bdata, PPO, cash=10000, commission=0.02)
stats = bt.run()
bt.plot()
print(stats)
[*********************100%***********************]  1 of 1 completed

png png png png

Start                     2015-01-02 00:00:00
End                       2022-11-18 00:00:00
Duration                   2877 days 00:00:00
Exposure Time [%]                   52.265861
Equity Final [$]                  1485.461341
Equity Peak [$]                  11330.885306
Return [%]                         -85.145387
Buy & Hold Return [%]              -22.316598
Return (Ann.) [%]                  -21.491087
Volatility (Ann.) [%]               21.490062
Sharpe Ratio                              0.0
Sortino Ratio                             0.0
Calmar Ratio                              0.0
Max. Drawdown [%]                  -88.917088
Avg. Drawdown [%]                  -28.010671
Max. Drawdown Duration     2571 days 00:00:00
Avg. Drawdown Duration      705 days 00:00:00
# Trades                                   71
Win Rate [%]                        26.760563
Best Trade [%]                      26.501533
Worst Trade [%]                    -22.092189
Avg. Trade [%]                       -2.73311
Max. Trade Duration          56 days 00:00:00
Avg. Trade Duration          20 days 00:00:00
Profit Factor                        0.496783
Expectancy [%]                      -2.346556
SQN                                 -2.054515
_strategy                                 PPO
_equity_curve                             ...
_trades                       Size  EntryB...
dtype: object

In first PPO strategy, we tested with AAPL and in second we tested with BABA. In BABA, we lost money but in AAPL we made some.

There are many features and strategy to try on stock backtesting using Backtesting.py and those will be covered in next part. Thank you 🙂

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