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barnabas Choppy multi-EWMA #36

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128 changes: 74 additions & 54 deletions data/leaderboard.json
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -60,6 +60,26 @@
"win_rate": 0.4847161572052402
}
},
{
"author_name": "Jim",
"strategy_name": "Enhanced Price Momentum",
"description": "Enhanced momentum strategy with trend confirmation, volatility-based position sizing, and risk management.",
"last_updated": "2025-05-17T12:50:15.376811Z",
"development_metrics": {
"sharpe": 1.5028615712789264,
"total_return": 83691.01305699791,
"max_drawdown": 0.8047181682279175,
"n_trades": 175,
"win_rate": 0.4685714285714286
},
"holdout_metrics": {
"sharpe": 2.3589800460822663,
"total_return": 0.25319737220833005,
"max_drawdown": 0.09536946074759874,
"n_trades": 8,
"win_rate": 0.75
}
},
{
"author_name": "Mahak",
"strategy_name": "Advanced Buy And Hold Strategy",
Expand Down Expand Up @@ -140,6 +160,26 @@
"win_rate": 0.25
}
},
{
"author_name": "Aditya",
"strategy_name": "SMA Crossover 1",
"description": "Goes long when the 30-period SMA crosses above the 120-period SMA, and exits when it crosses below.",
"last_updated": "2025-05-17T12:42:57.466976Z",
"development_metrics": {
"sharpe": 1.2890757652003817,
"total_return": 16908.019911407955,
"max_drawdown": 0.8924143378220439,
"n_trades": 584,
"win_rate": 0.4126712328767123
},
"holdout_metrics": {
"sharpe": 0.1546100984967055,
"total_return": -0.005179828456650437,
"max_drawdown": 0.2720590455045794,
"n_trades": 20,
"win_rate": 0.25
}
},
{
"author_name": "Will",
"strategy_name": "SMA Crossover 1",
Expand All @@ -160,26 +200,6 @@
"win_rate": 0.2962962962962963
}
},
{
"author_name": "Yuan",
"strategy_name": "Volume SMA Confirmation Strategy",
"description": "Goes long when the price breaks above the resistance with high volume, and exits when it breaks below the support with high volume.",
"last_updated": "2025-05-17T10:50:33.555705Z",
"development_metrics": {
"sharpe": 1.1497768598234075,
"total_return": 4483.2488594820325,
"max_drawdown": 0.8781365323919945,
"n_trades": 735,
"win_rate": 0.48707482993197276
},
"holdout_metrics": {
"sharpe": 0.7297079146266436,
"total_return": 0.07258730731958374,
"max_drawdown": 0.19649304246128135,
"n_trades": 26,
"win_rate": 0.46153846153846156
}
},
{
"author_name": "Lucien",
"strategy_name": "EMA Crossover + Adaptive ATR Filter",
Expand All @@ -200,26 +220,6 @@
"win_rate": 0.5263157894736842
}
},
{
"author_name": "Aditya",
"strategy_name": "SMA Crossover 1",
"description": "Goes long when the 30-period SMA crosses above the 120-period SMA, and exits when it crosses below.",
"last_updated": "2025-05-17T12:42:57.466976Z",
"development_metrics": {
"sharpe": 1.2890757652003817,
"total_return": 16908.019911407955,
"max_drawdown": 0.8924143378220439,
"n_trades": 584,
"win_rate": 0.4126712328767123
},
"holdout_metrics": {
"sharpe": 0.1546100984967055,
"total_return": -0.005179828456650437,
"max_drawdown": 0.2720590455045794,
"n_trades": 20,
"win_rate": 0.25
}
},
{
"author_name": "Will",
"strategy_name": "ML Enhanced Trading Strategy",
Expand All @@ -241,23 +241,43 @@
}
},
{
"author_name": "Jim",
"strategy_name": "Enhanced Price Momentum",
"description": "Enhanced momentum strategy with trend confirmation, volatility-based position sizing, and risk management.",
"last_updated": "2025-05-17T12:50:15.376811Z",
"author_name": "Yuan",
"strategy_name": "Volume SMA Confirmation Strategy",
"description": "Goes long when the price breaks above the resistance with high volume, and exits when it breaks below the support with high volume.",
"last_updated": "2025-05-17T10:50:33.555705Z",
"development_metrics": {
"sharpe": 1.5028615712789264,
"total_return": 83691.01305699791,
"max_drawdown": 0.8047181682279175,
"n_trades": 175,
"win_rate": 0.4685714285714286
"sharpe": 1.1497768598234075,
"total_return": 4483.2488594820325,
"max_drawdown": 0.8781365323919945,
"n_trades": 735,
"win_rate": 0.48707482993197276
},
"holdout_metrics": {
"sharpe": 2.3589800460822663,
"total_return": 0.25319737220833005,
"max_drawdown": 0.09536946074759874,
"n_trades": 8,
"win_rate": 0.75
"sharpe": 0.7297079146266436,
"total_return": 0.07258730731958374,
"max_drawdown": 0.19649304246128135,
"n_trades": 26,
"win_rate": 0.46153846153846156
}
},
{
"author_name": "Barnabas",
"strategy_name": "Multi EWMA Crossover (Choppy)",
"description": "Goes long when the fast EWMA crosses above the slow EWMA, and exits when it crosses below. Prevents trades when the market is choppy.",
"last_updated": "2025-05-17T17:57:56.951056Z",
"development_metrics": {
"sharpe": 1.1292850486779653,
"total_return": 4629.7711500823425,
"max_drawdown": 0.911266631874821,
"n_trades": 328,
"win_rate": 0.34146341463414637
},
"holdout_metrics": {
"sharpe": 0.5300982500415287,
"total_return": 0.04090393135053816,
"max_drawdown": 0.15072754709598601,
"n_trades": 7,
"win_rate": 0.2857142857142857
}
},
{
Expand Down
94 changes: 94 additions & 0 deletions src/strategies/barnabas-ewma.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,94 @@
import pandas as pd
import numpy as np

from src.core.strategy import Strategy

def choppiness(high: pd.Series,
low: pd.Series,
close: pd.Series,
length: int = 14) -> pd.Series:
"""
Choppiness Index (CHOP)

Parameters
----------
high, low, close : pd.Series
length : look-back period (default 14)

Returns
-------
pd.Series of CHOP values in the 0-100 range
"""
# True Range (vectorised)
tr = pd.concat([
high - low,
(high - close.shift(1)).abs(),
(low - close.shift(1)).abs()
], axis=1).max(axis=1)

sum_tr = tr.rolling(length).sum()
price_range = high.rolling(length).max() - low.rolling(length).min()

# Avoid divide-by-zero if range is zero
chop = 100 * np.log10(sum_tr / price_range.replace(0, np.nan)) / np.log10(length)
return chop

class BarnabasEWMA(Strategy):
def __init__(self, initial_capital=10000, fast=20, slow=200):
super().__init__(
initial_capital=initial_capital,
author_name="Barnabas",
strategy_name="Multi EWMA Crossover (Choppy)",
description="Goes long when the fast EWMA crosses above the slow EWMA, and exits when it crosses below. Prevents trades when the market is choppy."
)
self.prices = []
self.fast = fast
self.slow = slow
self.fast2 = fast * 2
self.slow2 = slow * 2
self.last_signal = 'hold'

def process_bar(self, bar):
"""
Process each bar of data.
This is where you implement your strategy logic.

Args:
bar: Dictionary containing 'time', 'close', and 'volume' data
"""
self.current_bar = bar

# Add your strategy logic here
# For example:
# if self.current_bar['close'] > self.previous_close * (1 + self.threshold):
# self.last_signal = 'buy'
# elif self.current_bar['close'] < self.previous_close * (1 - self.threshold):
# self.last_signal = 'sell'
# else:
# self.last_signal = 'hold'

def get_signal(self):
"""
Return the current trading signal.
Must return one of: 'buy', 'sell', 'hold'
"""
# Add your signal generation logic here
return 'hold'

def get_signals(self, df: pd.DataFrame) -> pd.Series:
"""
Vectorized version of signal generation.
Override this if you want to implement a more efficient vectorized version.
"""
fast_ma = df['close'].ewm(span=self.fast).mean()
slow_ma = df['close'].ewm(span=self.slow).mean()
fast2_ma = df['close'].ewm(span=self.fast2).mean()
slow2_ma = df['close'].ewm(span=self.slow2).mean()
signals = pd.Series('hold', index=df.index)
choppy = choppiness(df['high'], df['low'], df['close'], length=self.slow2)
signals[(fast_ma > slow_ma) & (fast2_ma > slow2_ma) & (choppy < 60)] = 'buy'
signals[(fast_ma < slow_ma)] = 'sell'
signals.iloc[:self.slow-1] = 'hold'

signals = signals.shift(1).fillna('hold')
return signals