Scripts calcul hyperopt multiple

tools/detect_regime.py

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+#!/usr/bin/env python3
+"""
+Detect bull/bear/range regimes from historical CSV data (SMA50/SMA200 crossovers)
+Usage: python3 detect_regime.py <PAIR> <CSV_FILE>
+CSV must contain at least columns: timestamp, close
+"""
+
+# python3 user_data/strategies/tools/detect_regime.py BTC /home/jerome/Perso/freqtradeDocker/user_data/data/binance/BTC_USDT-1d.feather
+
+import sys
+import pandas as pd
+import ta
+import talib.abstract as talib
+
+if len(sys.argv) < 3:
+    print("Usage: detect_regime.py <PAIR> <FEATHER_FILE>")
+    sys.exit(1)
+
+pair = sys.argv[1]
+file = sys.argv[2]
+
+# lecture du fichier feather
+df = pd.read_feather(file)
+# ne garder que timestamp et close pour detect_regime.py
+df[['date', 'close']].rename(columns={'date':'timestamp'}).to_csv(
+    f"user_data/data/{pair}-usdt-1d.csv", index=False
+)
+
+df = pd.read_csv(f"user_data/data/{pair}-usdt-1d.csv")
+
+if 'close' not in df.columns or 'timestamp' not in df.columns:
+    print("CSV must contain 'timestamp' and 'close' columns")
+    sys.exit(1)
+
+# --- paramètres ---
+SMOOTH_WIN = 5   # EMA pour lisser la pente
+NUM_TOP = 3      # nombre de segments à garder par classe
+
+# --- charger les données ---
+df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
+
+# --- calcul SMA14 ---
+df['sma14'] = ta.trend.sma_indicator(df['close'], 14)
+
+# --- pente brute ---
+df['slope'] = df['sma14'].diff()
+
+# --- lissage EMA ---
+df['slope_smooth'] = df['slope'].ewm(span=SMOOTH_WIN, adjust=False).mean()
+
+# --- normalisation relative ---
+df['slope_norm'] = df['slope_smooth'] / df['close']
+df['slope_norm'].fillna(0, inplace=True)
+
+# --- classification dynamique via quantiles ---
+q = df['slope_norm'].quantile([0.07, 0.21, 0.35, 0.65, 0.79, 0.93]).values
+q1, q2, q3, q4, q5, q6 = q
+
+def classify(v):
+    if v <= q1:
+        return 'BR3'
+    elif v <= q2:
+        return 'BR2'
+    elif v <= q3:
+        return 'BR1'
+    elif v <= q4:
+        return 'RG'
+    elif v <= q5:
+        return 'BU1'
+    elif v <= q6:
+        return 'BU2'
+    else:
+        return 'BU3'
+
+df['trend_class'] = df['slope_norm'].apply(classify)
+
+# --- boucle pour détecter les segments ---
+segments = []
+trend = None
+start_idx = None
+
+for i in range(len(df)):
+    new_trend = df['trend_class'].iloc[i]
+
+    if trend is None:
+        trend = new_trend
+        start_idx = i
+    elif new_trend != trend:
+        start_ts = df['timestamp'].iloc[start_idx]
+        end_ts = df['timestamp'].iloc[i]
+        segments.append((trend, start_ts, end_ts))
+        trend = new_trend
+        start_idx = i
+
+# fermer le dernier segment
+if trend is not None and start_idx is not None:
+    start_ts = df['timestamp'].iloc[start_idx]
+    end_ts = df['timestamp'].iloc[len(df)-1]
+    segments.append((trend, start_ts, end_ts))
+
+# --- extraire les 5 plus longs segments par classe ---
+top_segments_by_class = {}
+for cls in ['BR3','BR2','BR1','RG','BU1','BU2','BU3']:
+    cls_segments = [(t,s,e) for t,s,e in segments if t==cls]
+    # calcul de la durée
+    cls_segments = [(t,s,e,(e-s).total_seconds()) for t,s,e in cls_segments]
+    cls_segments.sort(key=lambda x: x[3], reverse=True)
+    top_segments_by_class[cls] = [(t,s,e) for t,s,e,d in cls_segments[:NUM_TOP]]
+
+# --- affichage ---
+for cls, segs in top_segments_by_class.items():
+    print(f"--- {cls} ---")
+    for t,s,e in segs:
+        print(f"{t} {s} {e}")