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Add bollinger strategy #23

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5640720
Add Bollinger Bands trading strategy
devashish24460 May 6, 2025
62da1c1
Add Bollinger Bands strategy results and summary
devashish24460 May 6, 2025
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68 changes: 68 additions & 0 deletions AAPL_backtest_summary.csv
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,0
Start,2024-01-02 00:00:00
End,2025-05-05 00:00:00
Duration,489 days 00:00:00
Exposure Time [%],16.36904761904762
Equity Final [$],8805.002468226288
Equity Peak [$],10381.560838839165
Commissions [$],703.7577009236095
Return [%],-11.949975317737117
Buy & Hold Return [%],6.405078007938552
Return (Ann.) [%],-9.103510330147934
Volatility (Ann.) [%],15.097975067477131
CAGR [%],-6.348004908513937
Sharpe Ratio,-0.6029623369664981
Sortino Ratio,-0.9818176087321079
Calmar Ratio,-0.4064775510211833
Alpha [%],-13.556641469466234
Beta,0.250842558004413
Max. Drawdown [%],-22.39609618606837
Avg. Drawdown [%],-9.291759364005102
Max. Drawdown Duration,216 days 00:00:00
Avg. Drawdown Duration,106 days 00:00:00
# Trades,18
Win Rate [%],50.0
Best Trade [%],3.8041930176504923
Worst Trade [%],-4.691479431007584
Avg. Trade [%],-0.3131544650804563
Max. Trade Duration,9 days 00:00:00
Avg. Trade Duration,3 days 00:00:00
Profit Factor,0.7201229237144986
Expectancy [%],-0.28801010963268897
SQN,-0.5212739426429492
Kelly Criterion,-0.18930867823349662
_strategy,BollingerBandsStrategy
_equity_curve," Equity DrawdownPct DrawdownDuration
Date
2024-01-02 10000.000000 0.000000 NaT
2024-01-03 10000.000000 0.000000 NaT
2024-01-04 10000.000000 0.000000 NaT
2024-01-05 10000.000000 0.000000 NaT
2024-01-08 10000.000000 0.000000 NaT
... ... ... ...
2025-04-29 8805.002468 0.151861 NaT
2025-04-30 8805.002468 0.151861 NaT
2025-05-01 8805.002468 0.151861 NaT
2025-05-02 8805.002468 0.151861 NaT
2025-05-05 8805.002468 0.151861 216 days

[336 rows x 3 columns]"
_trades," Size EntryBar ExitBar EntryPrice ExitPrice SL TP PnL ReturnPct EntryTime ExitTime Duration Tag Entry_λ(C) Exit_λ(C) Entry_λ(λ(C),λ(C)) Exit_λ(λ(C),λ(C))
0 58 43 47 169.957487 172.127235 None None 125.845376 0.012766 2024-03-05 2024-03-11 6 days None 4.527633 5.663169 173.048741 167.117081
1 -55 86 88 181.493040 181.990679 None None -27.370115 -0.002742 2024-05-06 2024-05-08 2 days None 4.763007 5.743741 160.837056 160.253583
2 -48 112 115 206.675374 212.655280 None None -287.035500 -0.028934 2024-06-12 2024-06-17 5 days None 6.014026 9.233323 180.991226 178.238745
3 -44 116 117 216.861138 213.213410 None None 160.500049 0.016821 2024-06-18 2024-06-20 2 days None 9.805533 9.984093 178.252929 178.759407
4 -42 131 132 230.614920 228.153193 None None 103.392568 0.010675 2024-07-11 2024-07-12 1 days None 7.516048 7.976927 201.493680 201.442496
5 48 149 151 204.612315 212.396162 None None 373.624675 0.038042 2024-08-06 2024-08-08 2 days None 7.301858 7.491392 207.374232 205.130431
6 47 178 179 215.276194 217.072249 None None 84.414566 0.008343 2024-09-17 2024-09-18 1 days None 3.698915 3.693978 215.793450 215.512965
7 -44 188 189 229.015955 225.393922 None None 159.369429 0.015816 2024-10-01 2024-10-02 1 days None 4.184788 4.166177 215.068660 215.401730
8 -44 199 200 231.091399 232.917370 None None -80.342753 -0.007902 2024-10-16 2024-10-17 1 days None 2.739419 2.876051 222.098788 221.989163
9 -43 203 204 233.376366 233.565951 None None -8.152167 -0.000812 2024-10-22 2024-10-23 1 days None 3.876052 3.816264 221.251383 221.589978
10 46 213 214 221.312919 222.121137 None None 37.178020 0.003652 2024-11-05 2024-11-06 1 days None 4.066379 4.390473 221.698410 220.709971
11 -43 229 235 234.212496 242.643218 None None -362.521083 -0.035996 2024-11-27 2024-12-06 9 days None 3.779119 6.652274 219.357893 218.878670
12 41 258 260 233.273523 234.382301 None None 45.459892 0.004753 2025-01-13 2025-01-15 2 days None 6.533045 7.651944 235.261381 231.777951
13 42 262 263 231.865066 223.753984 None None -340.665438 -0.034982 2025-01-17 2025-01-21 4 days None 9.160005 10.338023 226.450372 222.825231
14 42 264 265 219.548596 224.493183 None None 207.672666 0.022522 2025-01-22 2025-01-23 1 days None 11.100430 11.458420 220.003843 217.748055
15 -39 282 283 244.149994 244.660004 None None -19.890381 -0.002089 2025-02-18 2025-02-19 1 days None 6.862813 6.937019 218.889607 219.864920
16 42 297 301 223.809998 213.309998 None None -441.000000 -0.046915 2025-03-11 2025-03-17 6 days None 6.620664 12.090305 225.878672 209.865890
17 46 315 319 193.889999 189.070007 None None -221.719635 -0.024859 2025-04-04 2025-04-10 6 days None 8.593759 15.168659 199.377983 179.636684"
19 changes: 19 additions & 0 deletions AAPL_trades.csv
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Size,EntryBar,ExitBar,EntryPrice,ExitPrice,SL,TP,PnL,ReturnPct,EntryTime,ExitTime,Duration,Tag,Entry_λ(C),Exit_λ(C),"Entry_λ(λ(C),λ(C))","Exit_λ(λ(C),λ(C))"
58,43,47,169.95748724050745,172.12723509594798,,,125.84537561555095,0.012766415241066253,2024-03-05,2024-03-11,6 days,,4.527632575567383,5.663169097164208,173.0487410439336,167.11708068994892
-55,86,88,181.49304025440043,181.9906787165348,,,-27.37011541739065,-0.0027419148493894596,2024-05-06,2024-05-08,2 days,,4.763007498391218,5.743741067026366,160.83705644486795,160.25358308201172
-48,112,115,206.67537368785207,212.65527993528355,,,-287.0354998767107,-0.028933811226407258,2024-06-12,2024-06-17,5 days,,6.01402647066493,9.23332282211707,180.99122577571114,178.23874528594163
-44,116,117,216.86113841241473,213.21341001933183,,,160.5000492956476,0.016820572001913225,2024-06-18,2024-06-20,2 days,,9.805533381516616,9.984093209379408,178.25292936123435,178.75940681244236
-42,131,132,230.61492037011672,228.1531925563972,,,103.39256817622044,0.010674625083965439,2024-07-11,2024-07-12,1 days,,7.516048380054616,7.976926920488966,201.49367991225407,201.4424962566783
48,149,151,204.61231506511243,212.39616246807245,,,373.624675342081,0.03804193017650492,2024-08-06,2024-08-08,2 days,,7.30185805300242,7.4913924989886445,207.37423189204205,205.1304314021692
47,178,179,215.27619443719672,217.07224903728172,,,84.41456620399501,0.008343024665502297,2024-09-17,2024-09-18,1 days,,3.6989154418878405,3.693977513044931,215.79345024415403,215.5129650788906
-44,188,189,229.0159547825387,225.3939223013361,,,159.3694291729148,0.0158156338262192,2024-10-01,2024-10-02,1 days,,4.184788347706843,4.166176946026622,215.06865954115858,215.4017304280151
-44,199,200,231.09139869260778,232.9173703418766,,,-80.34275256782757,-0.007901512819599388,2024-10-16,2024-10-17,1 days,,2.739418600500189,2.8760508937308353,222.09878807365783,221.98916331873951
-43,203,204,233.376366009758,233.56595129753524,,,-8.15216737442168,-0.0008123585563473146,2024-10-22,2024-10-23,1 days,,3.876052441609934,3.8162640634156557,221.25138327595982,221.58997782409642
46,213,214,221.31291912362707,222.12113695305962,,,37.17802015389731,0.003651923406157165,2024-11-05,2024-11-06,1 days,,4.066378859333006,4.3904728217626126,221.69840988387307,220.70997079934585
-43,229,235,234.21249554010248,242.6432183998472,,,-362.5210829690229,-0.03599604214242791,2024-11-27,2024-12-06,9 days,,3.779119488016085,6.652274043551948,219.3578926157647,218.87866953374572
41,258,260,233.27352322575203,234.38230107288462,,,45.459891732436034,0.004753123422667871,2025-01-13,2025-01-15,2 days,,6.533044680637922,7.651944030740078,235.26138103667336,231.77795092777765
42,262,263,231.86506609768284,223.75398423747347,,,-340.6654381287934,-0.034981905626060295,2025-01-17,2025-01-21,4 days,,9.160004797200305,10.33802288197377,226.45037157015017,222.82523063375754
42,264,265,219.54859611049605,224.493183388126,,,207.67266566045788,0.022521607358133133,2025-01-22,2025-01-23,1 days,,11.100429518709664,11.458420096250016,220.00384295843028,217.74805467444332
-39,282,283,244.14999389648438,244.66000366210938,,,-19.890380859375,-0.0020889198377012086,2025-02-18,2025-02-19,1 days,,6.862813174493649,6.937019138117408,218.8896065001338,219.86492046399957
42,297,301,223.80999755859375,213.30999755859375,,,-441.0,-0.046914794310075836,2025-03-11,2025-03-17,6 days,,6.620663711930269,12.090304654568454,225.8786722709637,209.86589032465216
46,315,319,193.88999938964844,189.07000732421875,,,-221.71963500976562,-0.02485941554800486,2025-04-04,2025-04-10,6 days,,8.593758627796822,15.16865893252575,199.3779833852755,179.6366842101438
61 changes: 61 additions & 0 deletions BollingerBandsStrategy.html
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83 changes: 83 additions & 0 deletions strategy/bollinger.py
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import yfinance as yf
import numpy as np
import pandas as pd
from backtesting import Backtest, Strategy
import sys

# Custom rolling mean
def rolling_mean(x, window):
result = np.full_like(x, np.nan, dtype=np.float64)
for i in range(window - 1, len(x)):
result[i] = np.mean(x[i - window + 1:i + 1])
return result

# Custom rolling std
def rolling_std(x, window):
result = np.full_like(x, np.nan, dtype=np.float64)
for i in range(window - 1, len(x)):
result[i] = np.std(x[i - window + 1:i + 1])
return result

# ✅ Strategy class
class BollingerBandsStrategy(Strategy):
def init(self):
close = self.data.Close
window = 20
self.ma = self.I(lambda x: rolling_mean(x, window), close)
self.std = self.I(lambda x: rolling_std(x, window), close)
self.upper = self.I(lambda ma, std: ma + 2 * std, self.ma, self.std)
self.lower = self.I(lambda ma, std: ma - 2 * std, self.ma, self.std)

def next(self):
price = self.data.Close[-1]
ma, upper, lower = self.ma[-1], self.upper[-1], self.lower[-1]

if np.isnan(ma) or np.isnan(upper) or np.isnan(lower):
return

if price < lower:
self.buy()
elif price > upper:
self.sell()
elif self.position:
self.position.close()

# ✅ Backtest runner
def run_strategy(ticker="AAPL"):
print(f"\n📈 Running strategy on {ticker}...\n")
df = yf.download(ticker, start="2024-01-01", end="2025-05-06", auto_adjust=True)

# Flatten MultiIndex columns if needed
if isinstance(df.columns, pd.MultiIndex):
df.columns = df.columns.get_level_values(0) # Get the first level of the MultiIndex

# Ensure proper structure
try:
df = df[['Open', 'High', 'Low', 'Close', 'Volume']].dropna()
except KeyError as e:
print(f"❌ KeyError: {e}")
return

df.index.name = 'Date'

if df.empty:
print(f"❌ No data for {ticker}.")
return

bt = Backtest(df, BollingerBandsStrategy, cash=10000, commission=0.002)
stats = bt.run()

# Print key stats
print(stats[['Start', 'End', 'Duration', 'Return [%]', 'Sharpe Ratio', 'Max. Drawdown [%]', '# Trades']])

# Export results
stats['_trades'].to_csv(f"{ticker}_trades.csv", index=False)
stats.to_frame().to_csv(f"{ticker}_backtest_summary.csv")
print(f"\n✅ Exported: {ticker}_trades.csv and {ticker}_backtest_summary.csv")

bt.plot()

# ✅ Main entry
if __name__ == "__main__":
ticker = sys.argv[1] if len(sys.argv) > 1 else "AAPL"
run_strategy(ticker)