Update utils/forex_signals.py

utils/forex_signals.py

@@ -1,45 +1,71 @@
-import random
-from datetime import datetime
+import pandas as pd
+import numpy as np
+import talib
+from datetime import datetime, timedelta
 
-# Function to generate Forex signals
 def generate_forex_signals(trading_capital, market_risk, user_timezone, additional_pairs=None):
-    """
-    Generates trading signals for the forex market.
-    :param trading_capital: The amount of capital the user has for trading.
-    :param market_risk: The risk level selected by the user (Low, Medium, High).
-    :param user_timezone: The user's timezone.
-    :param additional_pairs: List of additional currency pairs to consider (optional).
-    :return: Dictionary of forex signals with relevant details.
-    """
-    
-    # Default list of currency pairs
-    default_pairs = [
-        "EUR/USD", "GBP/USD", "USD/JPY", "AUD/USD", "USD/CHF", 
-        "USD/CAD", "NZD/USD", "EUR/GBP", "EUR/JPY", "GBP/JPY"
-    ]
-    
-    # Add additional pairs if provided
-    if additional_pairs:
-        all_pairs = default_pairs + additional_pairs
-    else:
-        all_pairs = default_pairs
-    
-    # Example of generating signals (simplified logic for demo purposes)
+    # Placeholder: Retrieve historical data for each currency pair (e.g., from an API like Yahoo Finance or a local dataset)
+    # In practice, you'll fetch this data dynamically or from a database.
+    currency_pairs = additional_pairs if additional_pairs else ["EUR/USD", "GBP/USD", "AUD/USD"]  # Example pairs
     signals = []
-    for pair in all_pairs:
-        signal = {
-            "currency_pair": pair,
-            "entry_time": datetime.now().strftime("%Y-%m-%d %H:%M:%S"),
-            "exit_time": (datetime.now().strftime("%Y-%m-%d %H:%M:%S")),  # Simplified; should be dynamic
-            "roi": random.uniform(5, 15),  # Random ROI for demonstration
-            "signal_strength": random.uniform(50, 100)  # Random signal strength for demo
-        }
-        signals.append(signal)
-    
-    # Determine the best signal based on ROI (just a simplified example)
-    best_signal = max(signals, key=lambda x: x["roi"])
+
+    for pair in currency_pairs:
+        # Fetch historical data for the currency pair
+        # Placeholder: Load data from a file, API, or a database.
+        data = fetch_historical_data(pair)  # Replace with real function to fetch data
+
+        # Calculate technical indicators for entry/exit points
+        data['SMA_50'] = talib.SMA(data['close'], timeperiod=50)  # Simple Moving Average
+        data['SMA_200'] = talib.SMA(data['close'], timeperiod=200)
+        data['RSI'] = talib.RSI(data['close'], timeperiod=14)  # Relative Strength Index
+        data['BB_upper'], data['BB_middle'], data['BB_lower'] = talib.BBANDS(data['close'], timeperiod=20)
+
+        # Define strategy for entry and exit (example strategy)
+        entry_signal = None
+        exit_signal = None
+        entry_time = None
+        exit_time = None
+        max_roi = -float('inf')
+        signal_strength = 0
+
+        for i in range(200, len(data)):  # Skip first few rows due to moving average window
+            # Check if we should enter the market based on SMA crossover
+            if data['SMA_50'][i] > data['SMA_200'][i] and data['RSI'][i] < 30:  # Buy signal (bullish crossover)
+                entry_signal = "Buy"
+                entry_time = data.index[i]
+                entry_price = data['close'][i]
+
+                # Look ahead for the best exit signal within the next 2 hours (adjustable window)
+                for j in range(i+1, min(i+12, len(data))):  # Look 2 hours ahead (12 data points for 15-min intervals)
+                    if data['close'][j] > entry_price:  # Check if price has gone up
+                        roi = (data['close'][j] - entry_price) / entry_price * 100
+                        if roi > max_roi:
+                            max_roi = roi
+                            exit_signal = "Sell"
+                            exit_time = data.index[j]
+                            signal_strength = 100  # Simplified for now (could be refined further)
+            
+        # Append to the list of signals
+        if entry_signal and exit_signal:
+            signals.append({
+                'currency_pair': pair,
+                'entry_time': entry_time,
+                'exit_time': exit_time,
+                'roi': max_roi,
+                'signal_strength': signal_strength
+            })
 
     return {
-        "best_signal": best_signal,
-        "all_signals": signals
+        'best_signal': max(signals, key=lambda x: x['roi'], default={}),
+        'all_signals': signals
     }
+
+def fetch_historical_data(currency_pair):
+    # Placeholder function to fetch historical price data for a given currency pair
+    # Ideally, replace this with actual API calls to get real-time data
+    data = pd.DataFrame({
+        'date': pd.date_range(start="2025-01-01", periods=100, freq='15T'),
+        'close': np.random.rand(100) * 1.5 + 1.1  # Random price data (replace with actual data)
+    })
+    data.set_index('date', inplace=True)
+    return data