Update app.py

app.py

@@ -1,7 +1,7 @@
 import os
 import time
-import traceback
 import logging
+import traceback
 from datetime import datetime
 
 import gradio as gr
@@ -38,10 +38,12 @@ SYMBOLS = [
 WINDOW = 20
 
 MODEL_DIR = "models"
+DATA_DIR = "data"
+LOG_DIR = "logs"
 
 os.makedirs(MODEL_DIR, exist_ok=True)
-
-# TELEGRAM
+os.makedirs(DATA_DIR, exist_ok=True)
+os.makedirs(LOG_DIR, exist_ok=True)
 
 BOT_TOKEN = "YOUR_BOT_TOKEN"
 CHAT_ID = "YOUR_CHAT_ID"
@@ -58,26 +60,35 @@ logger = logging.getLogger("crypto_ai")
 
 # ============================================================
 
-# PATH
+# PATHS
 
 # ============================================================
 
 def model_path(symbol):
 
 
-    return os.path.join(
-        MODEL_DIR,
-        f"{symbol}_model.pkl"
-    )
+return os.path.join(
+    MODEL_DIR,
+    f"{symbol}_model.pkl"
+)
 
 
 def scaler_path(symbol):
 
 
-    return os.path.join(
-        MODEL_DIR,
-        f"{symbol}_scaler.pkl"
-    )
+return os.path.join(
+    MODEL_DIR,
+    f"{symbol}_scaler.pkl"
+)
+
+
+def data_path(symbol):
+
+
+return os.path.join(
+    DATA_DIR,
+    f"{symbol}.csv"
+)
 
 
 # ============================================================
@@ -109,6 +120,7 @@ try:
     )
 
 except Exception as e:
+
     logger.warning(e)
 
 
@@ -122,9 +134,15 @@ def clean_columns(df):
 
 
 if isinstance(df.columns, pd.MultiIndex):
-    df.columns = df.columns.get_level_values(0)
 
-df.columns = [str(c).lower() for c in df.columns]
+    df.columns = (
+        df.columns.get_level_values(0)
+    )
+
+df.columns = [
+    str(c).lower()
+    for c in df.columns
+]
 
 return df
 
@@ -147,6 +165,8 @@ try:
 
         df = clean_columns(df)
 
+        df.to_csv(data_path(symbol))
+
         return df, True
 
 except Exception as e:
@@ -168,7 +188,10 @@ def fetch_and_prepare(symbol):
 df, is_real = safe_download(symbol)
 
 if df is None:
-    return None, None, None, False
+
+    return None, None, None, None
+
+df.index = pd.to_datetime(df.index)
 
 df = df.resample("4h").agg({
     "open": "first",
@@ -200,10 +223,20 @@ df["rsi"] = (
 
 # MACD
 
-ema12 = df["close"].ewm(span=12).mean()
-ema26 = df["close"].ewm(span=26).mean()
+ema12 = (
+    df["close"]
+    .ewm(span=12)
+    .mean()
+)
+
+ema26 = (
+    df["close"]
+    .ewm(span=26)
+    .mean()
+)
 
 df["macd"] = ema12 - ema26
+
 df["macd_signal"] = (
     df["macd"]
     .ewm(span=9)
@@ -285,18 +318,6 @@ adx = ADXIndicator(
 
 df["adx"] = adx.adx()
 
-# TREND STRENGTH
-
-df["trend_strength"] = (
-    abs(
-        df["ema50"]
-        -
-        df["ema200"]
-    )
-    /
-    df["close"]
-)
-
 # VOLATILITY
 
 df["volatility"] = (
@@ -306,14 +327,16 @@ df["volatility"] = (
     .std()
 )
 
-# MARKET REGIME
+# REGIME
 
 def detect_regime(row):
 
     if row["volatility"] < 0.01:
+
         return "SIDEWAYS"
 
     if row["ema50"] > row["ema200"]:
+
         return "BULL"
 
     return "BEAR"
@@ -372,7 +395,6 @@ feat_cols = [
     "bb_lower",
     "atr",
     "adx",
-    "trend_strength",
     "return1",
     "return4"
 ]
@@ -386,6 +408,10 @@ for i in range(WINDOW, len(df)):
         i-WINDOW:i
     ][feat_cols].values
 
+    if np.isnan(window).any():
+
+        continue
+
     X_list.append(
         window.flatten()
     )
@@ -397,7 +423,7 @@ for i in range(WINDOW, len(df)):
 X = np.array(X_list)
 y = np.array(y_list)
 
-return X, y, df, feat_cols, is_real
+return X, y, df, feat_cols
 
 
 # ============================================================
@@ -409,11 +435,12 @@ return X, y, df, feat_cols, is_real
 def run_train(symbol):
 
 
-X, y, df, feat_cols, is_real = (
+X, y, df, feat_cols = (
     fetch_and_prepare(symbol)
 )
 
 if X is None:
+
     return
 
 scaler = MinMaxScaler()
@@ -490,9 +517,13 @@ def trade_levels(df, signal):
 
 last = df.iloc[-1]
 
-close_price = float(last["close"])
+close_price = float(
+    last["close"]
+)
 
-atr = float(last["atr"])
+atr = float(
+    last["atr"]
+)
 
 if signal == "BUY":
 
@@ -512,7 +543,10 @@ elif signal == "SELL":
 
 else:
 
-    tp1 = tp2 = tp3 = sl = close_price
+    tp1 = close_price
+    tp2 = close_price
+    tp3 = close_price
+    sl = close_price
 
 return {
     "entry": close_price,
@@ -540,7 +574,7 @@ if not os.path.exists(
 
     run_train(symbol)
 
-X, y, df, feat_cols, is_real = (
+X, y, df, feat_cols = (
     fetch_and_prepare(symbol)
 )
 
@@ -571,9 +605,10 @@ proba = model.predict_proba(
     latest_scaled
 )[0]
 
-confidence = float(
-    np.max(proba)
-) * 100
+confidence = (
+    float(np.max(proba))
+    * 100
+)
 
 last = df.iloc[-1]
 
@@ -583,12 +618,6 @@ bull_trend = (
     last["ema200"]
 )
 
-bear_trend = (
-    last["ema50"]
-    <
-    last["ema200"]
-)
-
 strong_volume = (
     last["volume"]
     >
@@ -639,6 +668,23 @@ levels = trade_levels(
     signal
 )
 
+# LOG
+
+with open(
+    os.path.join(
+        LOG_DIR,
+        "signals.log"
+    ),
+    "a"
+) as f:
+
+    f.write(
+        f"{datetime.utcnow()} | "
+        f"{symbol} | "
+        f"{signal} | "
+        f"{confidence:.2f}\n"
+    )
+
 # TELEGRAM
 
 previous = LAST_SIGNALS.get(symbol)
@@ -680,7 +726,7 @@ return {
 
 # ============================================================
 
-# SCAN ALL
+# REALTIME SCAN
 
 # ============================================================
 
@@ -693,7 +739,7 @@ global LAST_RETRAIN
 
 now = time.time()
 
-# AUTO RETRAIN
+# AUTO RETRAIN 6 JAM
 
 if now - LAST_RETRAIN > 21600:
 
@@ -717,7 +763,9 @@ for symbol in SYMBOLS:
 
     except Exception as e:
 
-        logger.error(traceback.format_exc())
+        logger.error(
+            traceback.format_exc()
+        )
 
 df = pd.DataFrame(rows)