utils/model_inference.py

import numpy as np
import pandas as pd
from datetime import datetime
import pytz

# Import your models and other necessary utilities here

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": currency_pair,
        "entry_time": entry_time,
        "exit_time": exit_time,
        "roi": roi,
        "signal_strength": signal_strength
    }