Update utils/forex_signals.py

utils/forex_signals.py

@@ -1,65 +1,82 @@
+import requests
 import pandas as pd
 import numpy as np
 
-# Define the function to calculate technical indicators
-
+# Function to calculate Simple Moving Average (SMA)
 def calculate_sma(data, window=14):
-    """Calculate Simple Moving Average (SMA)"""
-    return data['Close'].rolling(window=window).mean()
+    return data['close'].rolling(window=window).mean()
 
+# Function to calculate Relative Strength Index (RSI)
 def calculate_rsi(data, window=14):
-    """Calculate Relative Strength Index (RSI)"""
-    delta = data['Close'].diff()
+    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
 
+# Function to calculate Bollinger Bands
 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()
+    sma = calculate_sma(data)
+    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
 
-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 = []
+# Function to fetch Forex data from Financial Modeling Prep API
+def fetch_forex_data(pair, start_date='2020-01-01', end_date='2025-01-01', api_key='your_api_key'):
+    url = f'https://financialmodelingprep.com/api/v3/historical-price-full/{pair}?from={start_date}&to={end_date}&apikey={api_key}'
+    response = requests.get(url)
+    data = response.json()
+    
+    if 'historical' in data:
+        df = pd.DataFrame(data['historical'])
+        df['date'] = pd.to_datetime(df['date'])
+        df.set_index('date', inplace=True)
+        return df
+    else:
+        print(f"Error: No data available for {pair}.")
+        return pd.DataFrame()
 
-    # 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']
+# Function to generate Forex signals
+def generate_forex_signals(trading_capital, market_risk, user_timezone, additional_pairs=None, api_key='your_api_key'):
+    signals = []
+    
+    # List of currency pairs to generate signals for
+    currency_pairs = additional_pairs if additional_pairs else ['EURUSD', 'GBPUSD', 'USDJPY', 'AUDUSD']
+    
+    # Loop through each currency pair
     for pair in currency_pairs:
-        pair_data = data[data['Currency Pair'] == pair]
+        pair_data = fetch_forex_data(pair, api_key=api_key)
+        
+        if pair_data.empty:
+            continue  # Skip if no data is available
 
         # Calculate technical indicators
         sma = calculate_sma(pair_data)
         rsi = calculate_rsi(pair_data)
         upper_band, lower_band = calculate_bollinger_bands(pair_data)
 
-        # Example signal generation logic (this can be customized)
+        # Example signal generation logic (buy/sell based on RSI and Bollinger Bands)
         for i in range(len(pair_data)):
-            if rsi[i] < 30 and pair_data['Close'][i] < lower_band[i]:  # Buy signal
+            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
+                    'entry_time': pair_data.index[i],
+                    'exit_time': pair_data.index[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
+            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
+                    'entry_time': pair_data.index[i],
+                    'exit_time': pair_data.index[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 {}
 
+    # Find the best signal based on highest ROI
+    best_signal = max(signals, key=lambda x: x['roi']) if signals else {}
+    
     return {"best_signal": best_signal, "all_signals": signals}