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| 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 = [] | |
| 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': 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 | |