Update app.py

app.py

@@ -5,12 +5,13 @@ import traceback
 from datetime import datetime
 
 import gradio as gr
-import yfinance as yf
 import pandas as pd
 import numpy as np
 import joblib
 import requests
 
+from binance.client import Client
+
 from sklearn.preprocessing import MinMaxScaler
 from sklearn.model_selection import TimeSeriesSplit
 
@@ -24,14 +25,14 @@ from ta.trend import ADXIndicator
 # ============================================================
 
 SYMBOLS = [
-    "BTC-USD",
-    "ETH-USD",
-    "SOL-USD",
-    "BNB-USD",
-    "XRP-USD",
-    "DOGE-USD",
-    "AVAX-USD",
-    "LINK-USD",
+    "BTCUSDT",
+    "ETHUSDT",
+    "SOLUSDT",
+    "BNBUSDT",
+    "XRPUSDT",
+    "DOGEUSDT",
+    "AVAXUSDT",
+    "LINKUSDT",
 ]
 
 WINDOW = 20
@@ -47,6 +48,12 @@ os.makedirs(LOG_DIR, exist_ok=True)
 BOT_TOKEN = "YOUR_BOT_TOKEN"
 CHAT_ID = "YOUR_CHAT_ID"
 
+CACHE_SECONDS = 60
+
+LAST_DOWNLOAD = {}
+LAST_SIGNALS = {}
+LAST_RETRAIN = 0
+
 
 # ============================================================
 # LOGGING
@@ -57,6 +64,13 @@ logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger("crypto_ai")
 
 
+# ============================================================
+# BINANCE CLIENT
+# ============================================================
+
+client = Client()
+
+
 # ============================================================
 # PATHS
 # ============================================================
@@ -116,50 +130,21 @@ def send_telegram(message):
 
 
 # ============================================================
-# CLEAN COLUMNS
+# DOWNLOAD BINANCE DATA
 # ============================================================
 
-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
-    ]
-
-    return df
-
-
-# ============================================================
-# DOWNLOAD DATA
-# ============================================================
-
-# ============================================================
-# DOWNLOAD DATA WITH CACHE + RETRY
-# ============================================================
-
-LAST_DOWNLOAD = {}
-
-CACHE_SECONDS = 300
-
-
 def safe_download(symbol):
 
     try:
 
-        # ============================================
-        # CACHE
-        # ============================================
-
         now = time.time()
 
         csv_file = data_path(symbol)
 
+        # ============================================
+        # CACHE
+        # ============================================
+
         if os.path.exists(csv_file):
 
             last_time = LAST_DOWNLOAD.get(symbol, 0)
@@ -178,24 +163,37 @@ def safe_download(symbol):
         # DOWNLOAD
         # ============================================
 
-        df = yf.download(
-            symbol,
-            period="90d",
-            interval="1h",
-            progress=False,
-            auto_adjust=True,
-            threads=False
+        klines = client.get_klines(
+            symbol=symbol,
+            interval=Client.KLINE_INTERVAL_1HOUR,
+            limit=1000
         )
 
-        if df is not None and not df.empty:
+        rows = []
+
+        for k in klines:
+
+            rows.append({
+                "time": pd.to_datetime(
+                    k[0],
+                    unit="ms"
+                ),
+                "open": float(k[1]),
+                "high": float(k[2]),
+                "low": float(k[3]),
+                "close": float(k[4]),
+                "volume": float(k[5]),
+            })
+
+        df = pd.DataFrame(rows)
 
-            df = clean_columns(df)
+        df = df.set_index("time")
 
-            df.to_csv(csv_file)
+        df.to_csv(csv_file)
 
-            LAST_DOWNLOAD[symbol] = now
+        LAST_DOWNLOAD[symbol] = now
 
-            return df, True
+        return df, True
 
     except Exception as e:
 
@@ -204,7 +202,7 @@ def safe_download(symbol):
         )
 
     # ============================================
-    # FALLBACK TO CACHE
+    # FALLBACK CACHE
     # ============================================
 
     try:
@@ -212,7 +210,7 @@ def safe_download(symbol):
         if os.path.exists(csv_file):
 
             logger.info(
-                f"Using cached data: {symbol}"
+                f"Using cache: {symbol}"
             )
 
             df = pd.read_csv(
@@ -539,6 +537,8 @@ def train_all():
 
             run_train(symbol)
 
+            time.sleep(0.5)
+
         except Exception as e:
 
             logger.error(e)
@@ -585,11 +585,11 @@ def trade_levels(df, signal):
         sl = close_price
 
     return {
-        "entry": close_price,
-        "tp1": tp1,
-        "tp2": tp2,
-        "tp3": tp3,
-        "sl": sl
+        "entry": round(close_price, 2),
+        "tp1": round(tp1, 2),
+        "tp2": round(tp2, 2),
+        "tp3": round(tp3, 2),
+        "sl": round(sl, 2)
     }
 
 
@@ -597,8 +597,6 @@ def trade_levels(df, signal):
 # PREDICT
 # ============================================================
 
-LAST_SIGNALS = {}
-
 def run_predict(symbol):
 
     if not os.path.exists(
@@ -682,18 +680,12 @@ def run_predict(symbol):
 
     signal = "NO TRADE"
 
-    if regime == "SIDEWAYS":
-
-        signal = "NO TRADE"
-
-    else:
+    if regime != "SIDEWAYS":
 
         if pred == 1 and score >= 60:
-
             signal = "BUY"
 
         elif pred == 0 and score >= 60:
-
             signal = "SELL"
 
     levels = trade_levels(
@@ -716,6 +708,12 @@ PAIR: {symbol}
 SIGNAL: {signal}
 
 CONFIDENCE: {confidence:.2f}%
+
+ENTRY: {levels['entry']}
+TP1: {levels['tp1']}
+TP2: {levels['tp2']}
+TP3: {levels['tp3']}
+SL: {levels['sl']}
 """
         )
 
@@ -724,11 +722,11 @@ CONFIDENCE: {confidence:.2f}%
         "Signal": signal,
         "Confidence": round(confidence, 2),
         "Score": score,
-        "Entry": round(levels["entry"], 2),
-        "TP1": round(levels["tp1"], 2),
-        "TP2": round(levels["tp2"], 2),
-        "TP3": round(levels["tp3"], 2),
-        "SL": round(levels["sl"], 2),
+        "Entry": levels["entry"],
+        "TP1": levels["tp1"],
+        "TP2": levels["tp2"],
+        "TP3": levels["tp3"],
+        "SL": levels["sl"],
         "RSI": round(last["rsi"], 2),
         "ADX": round(last["adx"], 2),
         "Regime": regime
@@ -739,14 +737,14 @@ CONFIDENCE: {confidence:.2f}%
 # REALTIME SCAN
 # ============================================================
 
-LAST_RETRAIN = 0
-
 def realtime_scan():
 
     global LAST_RETRAIN
 
     now = time.time()
 
+    # AUTO RETRAIN EVERY 6 HOURS
+
     if now - LAST_RETRAIN > 21600:
 
         logger.info(
@@ -761,9 +759,10 @@ def realtime_scan():
 
     for symbol in SYMBOLS:
 
-        time.sleep(1)
         try:
 
+            time.sleep(0.3)
+
             result = run_predict(symbol)
 
             rows.append(result)
@@ -802,7 +801,7 @@ with gr.Blocks(
         interactive=False
     )
 
-    timer = gr.Timer(120)
+    timer = gr.Timer(30)
 
     timer.tick(
         fn=realtime_scan,