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.gitattributes

@@ -1,7 +1,2 @@
 *.keras filter=lfs diff=lfs merge=lfs -text
 *.joblib filter=lfs diff=lfs merge=lfs -text
-fvg_replay/results/consec_spot_2026-07-01_2026-07-21_vwap-none_gap0.csv filter=lfs diff=lfs merge=lfs -text
-fvg_replay/results/trades_month_all_lead.csv filter=lfs diff=lfs merge=lfs -text
-nwe_rsi/trades_phase3b.csv filter=lfs diff=lfs merge=lfs -text
-vwap_strategy/results/skips.csv filter=lfs diff=lfs merge=lfs -text
-vwap_strategy/results_nograce/skips.csv filter=lfs diff=lfs merge=lfs -text

indicators.py

@@ -1,76 +0,0 @@
-import numpy as np
-import pandas as pd
-
-
-def _wma(series: pd.Series, length: int) -> pd.Series:
-    weights = np.arange(1, length + 1)
-    return series.rolling(length).apply(
-        lambda x: np.dot(x, weights) / weights.sum(),
-        raw=True
-    )
-
-
-def add_strategy_indicators(df: pd.DataFrame, ema_fast: int = 5, ema_slow: int = 9) -> pd.DataFrame:
-    out = df.copy().sort_values("timestamp").reset_index(drop=True)
-
-    # Indicator 2. Column names stay ema5/ema9 (fast/slow roles) regardless of the
-    # configured span so existing NN feature names keep working when the period changes.
-    out["ema5"] = out["close"].ewm(span=ema_fast, adjust=False).mean()
-    out["ema9"] = out["close"].ewm(span=ema_slow, adjust=False).mean()
-
-    # Indicator 1: BB Stops using WMA(20) on OHLC4, mult=1.0
-    out["ohlc4"] = (out["open"] + out["high"] + out["low"] + out["close"]) / 4.0
-    out["bb_basis"] = _wma(out["ohlc4"], 20)
-    out["bb_dev"] = out["ohlc4"].rolling(20).std(ddof=0) * 1.0
-    out["bb_upper"] = out["bb_basis"] + out["bb_dev"]
-    out["bb_lower"] = out["bb_basis"] - out["bb_dev"]
-
-    up_flags = []
-    down_flags = []
-    phases = []
-    change_up = []
-    change_down = []
-
-    prev_up = False
-    prev_down = False
-
-    for i in range(len(out)):
-        curr_up = prev_up
-        curr_down = prev_down
-
-        if i > 0:
-            prev_src = out.loc[i - 1, "ohlc4"]
-            curr_src = out.loc[i, "ohlc4"]
-            prev_upper = out.loc[i - 1, "bb_upper"]
-            curr_upper = out.loc[i, "bb_upper"]
-            prev_lower = out.loc[i - 1, "bb_lower"]
-            curr_lower = out.loc[i, "bb_lower"]
-
-            if pd.notna(prev_upper) and pd.notna(curr_upper) and pd.notna(prev_lower) and pd.notna(curr_lower):
-                crossover = (prev_src <= prev_upper) and (curr_src > curr_upper)
-                crossunder = (prev_src >= prev_lower) and (curr_src < curr_lower)
-
-                if crossover:
-                    curr_up = True
-                    curr_down = False
-
-                if crossunder:
-                    curr_up = False
-                    curr_down = True
-
-        up_flags.append(curr_up)
-        down_flags.append(curr_down)
-        phases.append("green" if curr_up else "red" if curr_down else None)
-        change_up.append(prev_down and curr_up)
-        change_down.append(prev_up and curr_down)
-
-        prev_up = curr_up
-        prev_down = curr_down
-
-    out["bb_up"] = up_flags
-    out["bb_down"] = down_flags
-    out["bb_phase"] = phases
-    out["bb_change_up"] = change_up
-    out["bb_change_down"] = change_down
-
-    return out