Update utils/forex_signals.py

utils/forex_signals.py

@@ -1,71 +1,65 @@
 import pandas as pd
 import numpy as np
-import talib
-from datetime import datetime, timedelta
 
-def generate_forex_signals(trading_capital, market_risk, user_timezone, additional_pairs=None):
-    # 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 = []
+# Define the function to calculate technical indicators
 
-    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
+def calculate_sma(data, window=14):
+    """Calculate Simple Moving Average (SMA)"""
+    return data['Close'].rolling(window=window).mean()
 
-        # 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)
+def calculate_rsi(data, window=14):
+    """Calculate Relative Strength Index (RSI)"""
+    delta = data['Close'].diff()
+    gain = (delta.where(delta > 0, 0)).rolling(window=window).mean()
+    loss = (-delta.where(delta < 0, 0)).rolling(window=window).mean()
+    rs = gain / loss
+    rsi = 100 - (100 / (1 + rs))
+    return rsi
 
-        # 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
+def calculate_bollinger_bands(data, window=20, num_std_dev=2):
+    """Calculate Bollinger Bands"""
+    sma = calculate_sma(data, window)
+    rolling_std = data['Close'].rolling(window=window).std()
+    upper_band = sma + (rolling_std * num_std_dev)
+    lower_band = sma - (rolling_std * num_std_dev)
+    return upper_band, lower_band
 
-        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]
+def generate_forex_signals(trading_capital, market_risk, user_timezone, additional_pairs=None):
+    """Generate trading signals for the Forex market"""
+    # Example: Fetch historical data for currency pairs (use your data source here)
+    data = pd.read_csv('historical_forex_data.csv')  # Replace with real data source
+    signals = []
+
+    # Generate signals for multiple pairs (additional_pairs can be used to loop through them)
+    currency_pairs = additional_pairs if additional_pairs else ['EUR/USD', 'GBP/USD', 'USD/JPY', 'AUD/USD']
+    for pair in currency_pairs:
+        pair_data = data[data['Currency Pair'] == pair]
 
-                # 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
-            })
+        # Calculate technical indicators
+        sma = calculate_sma(pair_data)
+        rsi = calculate_rsi(pair_data)
+        upper_band, lower_band = calculate_bollinger_bands(pair_data)
 
-    return {
-        'best_signal': max(signals, key=lambda x: x['roi'], default={}),
-        'all_signals': signals
-    }
+        # Example signal generation logic (this can be customized)
+        for i in range(len(pair_data)):
+            if rsi[i] < 30 and pair_data['Close'][i] < lower_band[i]:  # Buy signal
+                signals.append({
+                    'currency_pair': pair,
+                    'entry_time': pair_data['Date'][i],
+                    'exit_time': pair_data['Date'][i] + pd.Timedelta(hours=2),  # Example exit time
+                    'roi': np.random.uniform(0.1, 1),  # Simulated ROI, adjust accordingly
+                    'signal_strength': np.random.uniform(50, 100)  # Simulated signal strength
+                })
+            elif rsi[i] > 70 and pair_data['Close'][i] > upper_band[i]:  # Sell signal
+                signals.append({
+                    'currency_pair': pair,
+                    'entry_time': pair_data['Date'][i],
+                    'exit_time': pair_data['Date'][i] + pd.Timedelta(hours=2),  # Example exit time
+                    'roi': np.random.uniform(0.1, 1),  # Simulated ROI, adjust accordingly
+                    'signal_strength': np.random.uniform(50, 100)  # Simulated signal strength
+                })
+    
+    # Choose the best signal
+    best_signal = max(signals, key=lambda x: x['roi']) if signals else {}
 
-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
+    return {"best_signal": best_signal, "all_signals": signals}