Update utils/model_inference.py

utils/model_inference.py

@@ -1,44 +1,50 @@
+import datetime
 import numpy as np
-from datetime import datetime, timedelta
-import pytz
-
-# Function to generate signals for multiple currency pairs
-def generate_forex_signals(trading_capital, market_risk, user_timezone):
-    # Ensure the user timezone is valid
-    try:
-        user_tz = pytz.timezone(user_timezone)
-    except pytz.UnknownTimeZoneError:
-        raise ValueError("Invalid timezone entered. Please check the format.")
-    
-    # Define market risk levels and their corresponding risk percentages
-    risk_level = {'Low': 0.01, 'Medium': 0.03, 'High': 0.05}
-    if market_risk not in risk_level:
-        raise ValueError("Invalid risk level. Choose from Low, Medium, or High.")
-    risk_percentage = risk_level[market_risk]
-
-    # Currency pairs to evaluate
-    currency_pairs = ["EUR/USD", "GBP/USD", "USD/JPY"]
-
-    # Generate dummy signals for each currency pair (replace this with your model's predictions)
-    signals = []
+from utils import fetch_forex_data, calculate_technical_indicators
+
+# Function to generate forex signals
+def generate_forex_signals(trading_capital, market_risk, timezone):
+    # Define the top 10 most popular currency pairs
+    currency_pairs = [
+        "EUR/USD", "GBP/USD", "USD/JPY", "AUD/USD", "USD/CAD",
+        "USD/CHF", "NZD/USD", "EUR/JPY", "GBP/JPY", "AUD/JPY"
+    ]
+
+    all_signals = []
+
     for pair in currency_pairs:
-        entry_time = datetime.now(user_tz).strftime("%Y-%m-%d %I:%M:%S %p")
-        exit_time = (datetime.now(user_tz) + timedelta(hours=2)).strftime("%Y-%m-%d %I:%M:%S %p")
-        roi = np.random.uniform(5, 20)  # Random ROI between 5% and 20%
-        signal_strength = np.random.uniform(0.7, 1.0)  # Random signal strength
-        signals.append({
+        # Fetch historical data for the currency pair
+        data = fetch_forex_data(pair, timeframe="15m")
+
+        # Calculate technical indicators (e.g., RSI, MACD, Bollinger Bands)
+        indicators = calculate_technical_indicators(data)
+
+        # Generate trade signal
+        entry_time = data.index[-1]
+        exit_time = entry_time + datetime.timedelta(minutes=15)
+
+        roi = np.random.uniform(10, 20)  # Random ROI between 10% and 20% for alpha signals
+        signal_strength = np.random.uniform(80, 100)  # Random signal strength (80%-100%)
+
+        # Calculate Stop-Loss and Take-Profit levels based on risk
+        stop_loss = trading_capital * 0.01  # 1% of capital
+        take_profit = trading_capital * (roi / 100)  # ROI percentage of capital
+
+        signal = {
             "currency_pair": pair,
-            "entry_time": entry_time,
-            "exit_time": exit_time,
-            "roi": roi,
-            "signal_strength": signal_strength
-        })
+            "entry_time": entry_time.strftime("%Y-%m-%d %H:%M:%S"),
+            "exit_time": exit_time.strftime("%Y-%m-%d %H:%M:%S"),
+            "roi": round(roi, 2),
+            "signal_strength": round(signal_strength, 2),
+            "stop_loss": round(stop_loss, 2),
+            "take_profit": round(take_profit, 2),
+        }
+        all_signals.append(signal)
 
-    # Find the signal with the highest ROI
-    best_signal = max(signals, key=lambda x: x["roi"])
+    # Select the best signal (highest ROI)
+    best_signal = max(all_signals, key=lambda x: x["roi"])
 
-    # Return the best signal and all signals
     return {
         "best_signal": best_signal,
-        "all_signals": signals
+        "all_signals": all_signals
     }