5f7702469e
- Removed v10 TradingView indicator (moneyline_v10_momentum_dots.pinescript) - Removed v10 penalty system from signal-quality.ts (-30/-25 point penalties) - Removed backtest result files (sweep_*.csv) - Updated copilot-instructions.md to remove v10 references - Simplified direction-specific quality thresholds (LONG 90+, SHORT 80+) Rationale: - 1,944 parameter combinations tested in backtest - All top results IDENTICAL (568 trades, $498 P&L, 61.09% WR) - Momentum parameters had ZERO impact on trade selection - Profit factor 1.027 too low (barely profitable after fees) - Max drawdown -$1,270 vs +$498 profit = terrible risk-reward - v10 penalties were blocking good trades (bug: applied to wrong positions) Keeping v9 as production system - simpler, proven, effective.
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
from __future__ import annotations
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import numpy as np
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import pandas as pd
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def rma(series: pd.Series, length: int) -> pd.Series:
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alpha = 1.0 / length
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result = series.astype(float).copy()
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for i in range(1, len(series)):
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prev = result.iat[i - 1]
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curr = series.iat[i]
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result.iat[i] = alpha * curr + (1 - alpha) * prev
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return result
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def calculate_atr(df: pd.DataFrame, length: int) -> pd.Series:
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high, low, close = df["high"], df["low"], df["close"]
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tr = pd.concat([
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(high - low),
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(high - close.shift(1)).abs(),
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(low - close.shift(1)).abs(),
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], axis=1).max(axis=1)
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tr.iloc[0] = (high.iloc[0] - low.iloc[0])
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return rma(tr, length)
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def calculate_adx(df: pd.DataFrame, length: int) -> pd.Series:
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high, low, close = df["high"], df["low"], df["close"]
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up_move = high.diff()
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down_move = -low.diff()
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plus_dm = np.where((up_move > down_move) & (up_move > 0), up_move, 0.0)
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minus_dm = np.where((down_move > up_move) & (down_move > 0), down_move, 0.0)
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tr = pd.concat([
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(high - low),
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(high - close.shift(1)).abs(),
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(low - close.shift(1)).abs(),
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], axis=1).max(axis=1)
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tr.iloc[0] = (high.iloc[0] - low.iloc[0])
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atr = rma(tr, length)
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plus_di = 100.0 * rma(pd.Series(plus_dm), length) / atr
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minus_di = 100.0 * rma(pd.Series(minus_dm), length) / atr
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dx = 100.0 * (plus_di - minus_di).abs() / (plus_di + minus_di).replace(0, np.nan)
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dx = dx.fillna(0.0)
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return rma(dx, length)
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