Update utils/forex_signals.py

utils/forex_signals.py

@@ -1,82 +1,63 @@
 import requests
-import pandas as pd
-import numpy as np
-
-# Function to calculate Simple Moving Average (SMA)
-def calculate_sma(data, window=14):
-    return data['close'].rolling(window=window).mean()
-
-# Function to calculate Relative Strength Index (RSI)
-def calculate_rsi(data, window=14):
-    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):
-    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
-
-# 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='89SEdLScHxHk6j8J9OoH4sLFS3Mri4oW'):
-    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()
-
-# Function to generate Forex signals
-def generate_forex_signals(trading_capital, market_risk, user_timezone, additional_pairs=None, api_key='your_api_key'):
+import random
+from datetime import datetime, timedelta
+
+API_KEY = "89SEdLScHxHk6j8J9OoH4sLFS3Mri4oW"
+BASE_URL = "https://financialmodelingprep.com/api/v3/forex"
+
+def fetch_forex_data(pair):
+    """Fetch historical forex data for a given currency pair."""
+    try:
+        url = f"{BASE_URL}/{pair}?apikey={API_KEY}"
+        response = requests.get(url)
+
+        if response.status_code == 200:
+            data = response.json()
+            if not data or "historical" not in data:
+                return None  # No data available for this pair
+            return data["historical"]
+        else:
+            print(f"Error fetching data for {pair}: {response.status_code} - {response.text}")
+            return None
+    except Exception as e:
+        print(f"Exception while fetching data for {pair}: {str(e)}")
+        return None
+
+def generate_forex_signals(trading_capital, market_risk, user_timezone, additional_pairs):
+    """Generate forex trading signals."""
     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 = 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 (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
-                signals.append({
-                    'currency_pair': pair,
-                    '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
-                signals.append({
-                    'currency_pair': pair,
-                    '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
-                })
-
-    # 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}
+    for pair in additional_pairs:
+        print(f"Fetching data for {pair}...")
+        data = fetch_forex_data(pair)
+
+        if data is None:
+            print(f"Skipping {pair} due to missing data.")
+            continue  # Skip to the next pair if data is missing
+
+        # Simulate processing data for signal generation
+        entry_time = datetime.now()
+        exit_time = entry_time + timedelta(hours=random.randint(1, 5))  # Simulated exit time
+        roi = round(random.uniform(0.5, 10.0), 2)  # Simulated ROI
+        signal_strength = round(random.uniform(50, 100), 2)  # Simulated signal strength
+
+        signals.append({
+            "currency_pair": pair,
+            "entry_time": entry_time.strftime("%Y-%m-%d %H:%M:%S"),
+            "exit_time": exit_time.strftime("%Y-%m-%d %H:%M:%S"),
+            "roi": roi,
+            "signal_strength": signal_strength,
+        })
+
+    # Sort signals by ROI (descending) to recommend the best signal
+    signals.sort(key=lambda x: x["roi"], reverse=True)
+
+    if signals:
+        return {
+            "best_signal": signals[0],
+            "all_signals": signals,
+        }
+    else:
+        return {
+            "best_signal": None,
+            "all_signals": [],
+        }