Create MLCryptoForecasterAllAssetsTPSL_ParisTime.py

MLCryptoForecasterAllAssetsTPSL_ParisTime.py

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+import os
+import pandas as pd
+import numpy as np
+from datetime import timedelta
+from binance.client import Client
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import classification_report
+import ta
+import pytz
+
+# Function to log results to both console and file
+# Blank lines are added after each asset block explicitly
+
+def log_results(message, filename="predictions_results.txt"):
+    print(message)
+    with open(filename, "a") as f:
+        f.write(message + "\n")
+
+# Convert UTC timestamp to Europe/Paris timezone
+
+def convert_to_paris_time(utc_time):
+    paris_tz = pytz.timezone('Europe/Paris')
+    utc_time = utc_time.replace(tzinfo=pytz.utc)
+    paris_time = utc_time.astimezone(paris_tz)
+    return paris_time.strftime('%Y-%m-%d %H:%M:%S')
+
+# Initialize Binance client
+client = Client()
+
+# Settings
+interval = Client.KLINE_INTERVAL_4HOUR
+result_file = "predictions_results.txt"
+
+# Delete the results file if it exists for a fresh start
+if os.path.exists(result_file):
+    os.remove(result_file)
+
+# Initialize result file header
+with open(result_file, "w") as f:
+    f.write("Asset,Time,Price,Prediction,Optimal_UP_TP,Optimal_UP_SL,Optimal_DN_TP,Optimal_DN_SL\n")
+
+# Get USDT-quoted trading symbols
+symbols = [s['symbol'] for s in client.get_exchange_info()['symbols']
+           if s['status']=='TRADING' and s['quoteAsset']=='USDT']
+
+# Optimize take-profit / stop-loss function
+def optimize_tp_sl(df, signals, side, pgrid, lgrid):
+    best = (0, 0, -np.inf)
+    prices = df['close'].values
+    idxs = np.where(signals == side)[0]
+    for tp in pgrid:
+        for sl in lgrid:
+            rets = []
+            for i in idxs:
+                entry = prices[i]
+                for j in range(i+1, min(i+11, len(prices))):
+                    ret = (prices[j] - entry) / entry if side == 1 else (entry - prices[j]) / entry
+                    if ret >= tp or ret <= -sl:
+                        rets.append(np.sign(ret) * min(abs(ret), max(tp, sl)))
+                        break
+            if rets:
+                avg_ret = np.mean(rets)
+                if avg_ret > best[2]:
+                    best = (tp, sl, avg_ret)
+    return best
+
+# Main loop: process each symbol
+for symbol in symbols:
+    log_results(f"=== {symbol} ===", result_file)
+
+    # Load or download historical data
+    data_file = f"{symbol}_data_4h_full.csv"
+    if os.path.exists(data_file):
+        df = pd.read_csv(data_file, index_col=0, parse_dates=True)
+        last_ts = df.index[-1]
+        start = (last_ts + timedelta(hours=4)).strftime("%d %B %Y %H:%M:%S")
+        new = client.get_historical_klines(symbol, interval, start)
+        if new:
+            new_df = pd.DataFrame(new, columns=[
+                'timestamp','open','high','low','close','volume',
+                'close_time','quote_av','trades','tb_base_av','tb_quote_av','ignore'
+            ])
+            new_df = new_df[['timestamp','open','high','low','close','volume']].astype(float)
+            new_df['timestamp'] = pd.to_datetime(new_df['timestamp'], unit='ms')
+            new_df.set_index('timestamp', inplace=True)
+            df = pd.concat([df, new_df]).drop_duplicates()
+            df.to_csv(data_file)
+    else:
+        klines = client.get_historical_klines(symbol, interval, "01 December 2021")
+        df = pd.DataFrame(klines, columns=[
+            'timestamp','open','high','low','close','volume',
+            'close_time','quote_av','trades','tb_base_av','tb_quote_av','ignore'
+        ])
+        df = df[['timestamp','open','high','low','close','volume']].astype(float)
+        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+        df.set_index('timestamp', inplace=True)
+        df.to_csv(data_file)
+
+    # Compute technical indicators
+    df['rsi'] = ta.momentum.RSIIndicator(df['close'], window=14).rsi()
+    df['macd'] = ta.trend.MACD(df['close']).macd()
+    for s in [10, 20, 50, 100]:
+        df[f'ema_{s}'] = df['close'].ewm(span=s).mean()
+    for w in [10, 20, 50, 100]:
+        df[f'sma_{w}'] = df['close'].rolling(window=w).mean()
+    bb = ta.volatility.BollingerBands(df['close'], window=20, window_dev=2)
+    df['bbw'] = (bb.bollinger_hband() - bb.bollinger_lband()) / bb.bollinger_mavg()
+    df['atr'] = ta.volatility.AverageTrueRange(df['high'], df['low'], df['close'], window=14).average_true_range()
+    df['adx'] = ta.trend.ADXIndicator(df['high'], df['low'], df['close'], window=14).adx()
+    st = ta.momentum.StochasticOscillator(df['high'], df['low'], df['close'], window=14)
+    df['st_k'] = st.stoch()
+    df['st_d'] = st.stoch_signal()
+    df['wr'] = ta.momentum.WilliamsRIndicator(df['high'], df['low'], df['close'], lbp=14).williams_r()
+    df['cci'] = ta.trend.CCIIndicator(df['high'], df['low'], df['close'], window=20).cci()
+    df['mom'] = df['close'] - df['close'].shift(10)
+    ichi = ta.trend.IchimokuIndicator(df['high'], df['low'], window1=9, window2=26, window3=52)
+    df['span_a'] = ichi.ichimoku_a()
+    df['span_b'] = ichi.ichimoku_b()
+    df.dropna(inplace=True)
+
+    # Label signals based on Ichimoku cloud
+    df['signal'] = np.select(
+        [(df['close'] > df['span_a']) & (df['close'] > df['span_b']),
+         (df['close'] < df['span_a']) & (df['close'] < df['span_b'])],
+        [1, 0], default=-1)
+
+    # Train/test split
+    features = [c for c in df.columns if c not in ['open','high','low','close','volume','signal']]
+    X, y = df[features], df['signal']
+    Xtr, Xte, ytr, yte = train_test_split(X, y, test_size=0.2, shuffle=False)
+    model = RandomForestClassifier(n_estimators=200, class_weight='balanced', random_state=42)
+    model.fit(Xtr, ytr)
+    ypr = model.predict(Xte)
+
+    # Log classification report
+    report = classification_report(yte, ypr, zero_division=0)
+    log_results(f"Classification report for {symbol}:\n{report}", result_file)
+
+    # Predict latest trend with correct feature naming
+    latest_df = X.iloc[-1:]
+    trend_label = model.predict(latest_df)[0]
+
+    # Convert timestamp to Paris time and fetch price
+    pred_time_utc = df.index[-1]
+    pred_time = convert_to_paris_time(pred_time_utc)
+    pred_price = df['close'].iloc[-1]
+    trend_str = {1:'Uptrend',0:'Downtrend',-1:'Neutral'}[trend_label]
+    log_results(f"Time: {pred_time}, Price: {pred_price:.2f}, Prediction: {trend_str}", result_file)
+
+    # Optimize TP/SL and log results
+    hist_sign = model.predict(X)
+    pgrid = np.arange(0.01, 0.1, 0.01)
+    lgrid = np.arange(0.01, 0.1, 0.01)
+    up_tp, up_sl, _ = optimize_tp_sl(df, hist_sign, 1, pgrid, lgrid)
+    dn_tp, dn_sl, _ = optimize_tp_sl(df, hist_sign, 0, pgrid, lgrid)
+    log_results(f"Optimal UP TP/SL: +{up_tp*100:.1f}% / -{up_sl*100:.1f}%", result_file)
+    log_results(f"Optimal DN TP/SL: +{dn_tp*100:.1f}% / -{dn_sl*100:.1f}%", result_file)
+
+    # Blank line after asset
+    with open(result_file, "a") as f:
+        f.write("\n")
+
+# End of processing
+log_results("All assets processed.", result_file)