utils/model_inference.py

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 = []
    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({
            "currency_pair": pair,
            "entry_time": entry_time,
            "exit_time": exit_time,
            "roi": roi,
            "signal_strength": signal_strength
        })

    # Find the signal with the highest ROI
    best_signal = max(signals, key=lambda x: x["roi"])

    # Return the best signal and all signals
    return {
        "best_signal": best_signal,
        "all_signals": signals
    }