Update utils/model_inference.py

utils/model_inference.py

@@ -1,26 +1,42 @@
-from config import RISK_LEVELS
-import random
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import pytz
 
-def generate_forex_signals(capital, risk_level):
-    """
-    Generate signals based on trading capital and risk level.
-    """
-    # Fetch stop loss and take profit percentages based on risk level
-    risk_params = RISK_LEVELS[risk_level]
-    stop_loss = risk_params["stop_loss"]
-    take_profit = risk_params["take_profit"]
+# Import your models and other necessary utilities here
 
-    # Simulate ROI based on risk level and trading capital
-    roi = random.uniform(take_profit * 80, take_profit * 120)  # Randomized ROI in range
-    signal_strength = "High" if roi > (take_profit * 100) else "Medium"
-
-    # Entry and exit logic
-    entry_time = "Generated dynamically in app.py"  # Placeholder
+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.")
+    
+    # Example of how you might process trading capital and risk level:
+    # Assume this logic is based on the user input for market risk
+    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, High.")
+    
+    risk_percentage = risk_level[market_risk]
+    
+    # Perform model inference based on the user's inputs:
+    # For example, load the model and predict
+    # signal = model.predict(features)
+    
+    # Dummy signal generation (Replace with your model inference logic)
+    currency_pair = "EUR/USD"
+    entry_time = datetime.now(user_tz).strftime("%Y-%m-%d %H:%M:%S")
+    exit_time = (datetime.now(user_tz) + pd.Timedelta(hours=2)).strftime("%Y-%m-%d %H:%M:%S")
+    roi = np.random.uniform(5, 15)  # Random ROI between 5% and 15%
+    signal_strength = np.random.uniform(0.7, 1.0)  # Random strength between 0.7 and 1.0
 
+    # Return the result as a dictionary
     return {
-        "currency_pair": random.choice(["EUR/USD", "GBP/USD", "USD/JPY", "AUD/USD", "USD/CHF"]),
+        "currency_pair": currency_pair,
         "entry_time": entry_time,
-        "exit_time": "Entry time + 2 hours",
-        "roi": round(roi, 2),
+        "exit_time": exit_time,
+        "roi": roi,
         "signal_strength": signal_strength
     }