main.py

# Fahrenheit Black Quantitative Trading System

import requests
import pandas as pd
import boto3
from typing import List, Dict, Series

class DataRetrieval:
    def __init__(self, api_key):
        self.api_key = api_key

    def fetch_realtime_data(self, symbol):
        url = f'https://api.twelvedata.com/time_series?symbol={symbol}&interval=1d&apikey={self.api_key}'
        response = requests.get(url)

        if response.status_code == 200:
            data = response.json()
            df = pd.DataFrame(data['values'])
            df['datetime'] = pd.to_datetime(df['datetime'])
            df.set_index('datetime', inplace=True)
            return df
        else:
            raise Exception(f"Error fetching real-time data. Status code: {response.status_code}")

    def fetch_historical_data(self, symbol, start_date, end_date):
        url = f'https://api.twelvedata.com/time_series?symbol={symbol}&interval=1d&start_date={start_date}&end_date={end_date}&apikey={self.api_key}'
        response = requests.get(url)

        if response.status_code == 200:
            data = response.json()
            df = pd.DataFrame(data['values'])
            df['datetime'] = pd.to_datetime(df['datetime'])
            df.set_index('datetime', inplace=True)
            return df
        else:
            raise Exception(f"Error fetching historical data. Status code: {response.status_code}")

            # Example Usage
            api_key = 'YOUR_API_KEY_HERE'  # Replace with your Twelve Data API key
            data_retrieval = DataRetrieval(api_key)
            
            # Fetch real-time data for TSLA
            realtime_data = data_retrieval.fetch_realtime_data('TSLA')
            print("Real-time Data:")
            print(realtime_data.head())
            
            # Fetch historical data for TSLA (e.g., from 2022-01-01 to 2022-10-20)
            start_date = '2022-01-01'
            end_date = '2022-10-20'
            historical_data = data_retrieval.fetch_historical_data('TSLA', start_date, end_date)
            print("\nHistorical Data:")
            print(historical_data.head())


class DataStorage:
    """
    DataStorage handles the storage and retrieval of data in CSV format.
    It provides functions to save data as a CSV file and upload to an S3 bucket.
    """
    def __init__(self, data_path):
        self.data_path = data_path

    def save_to_csv(self, data, filename):
        """
        Saves DataFrame data to a CSV file.
        """
        data.to_csv(f"{self.data_path}/{filename}.csv", index=False)

    def load_from_csv(self, filename, bucket_name):
        """
        Loads data from a CSV file into a DataFrame and stores into an S3 bucket.
        """
        df = pd.read_csv(f"{self.data_path}/{filename}.csv")
        s3 = boto3.client('s3')
        s3.upload_file(f"{self.data_path}/{filename}.csv", bucket_name, f"{filename}.csv")

# Example Usage
data_storage = DataStorage(data_path="/path/to/data")

# Assuming you have fetched historical data using DataRetrieval
historical_data = pd.DataFrame({
    'Date': ['2022-01-01', '2022-01-02', '2022-01-03'],
    'Open': [700.0, 710.0, 720.0],
    'Close': [705.0, 715.0, 725.0]
})

# Save data as CSV and upload to S3 bucket
data_storage.save_to_csv(historical_data, "TSLA_Historical_Data")
data_storage.load_from_csv("TSLA_Historical_Data", "FarBlack")

class DataProcessing:
    """
    DataProcessing performs data cleaning and transformation operations.
    It provides functions to clean and transform data.
    """
    def __init__(self, data):
        self.data = data

    def clean_data(self):
        """
        Cleans and preprocesses the loaded data.
        """
        # Assuming 'Date' is the column name for date and 'Close' is the column for closing price
        self.data['Date'] = pd.to_datetime(self.data['Date'])
        self.data.dropna(inplace=True)  # Remove rows with missing values
        self.data.reset_index(drop=True, inplace=True)

    def transform_data(self):
        """
        Applies necessary transformations or calculations to the data.
        """
        # Example transformation: Calculate daily returns
        self.data['Daily_Return'] = self.data['Close'].pct_change() * 100  # Multiply by 100 for percentage

# Example Usage
data_processing = DataProcessing(data=pd.read_csv('TSLA_Historical_Data.csv'))

# Clean and preprocess the data
data_processing.clean_data()

# Apply necessary transformations
data_processing.transform_data()

# Print the modified DataFrame
print(data_processing.data.head())

# 2. Trading Strategies Module

class StrategyInterface:
    """
    StrategyInterface defines the interface for trading strategies.
    It provides functions to analyze data and generate trading signals.
    """
    def __init__(self, data):
        self.data = data
        self.signals = None

    def analyze(self, short_window=20, long_window=50):
        """
        Analyzes data and generates trading signals based on moving average crossover.
        Returns a Series containing trading signals (1 for buy, -1 for sell, 0 for hold).
        """
        short_ma = self.data['Close'].rolling(window=short_window).mean()
        long_ma = self.data['Close'].rolling(window=long_window).mean()

        # Generate signals
        self.signals = pd.Series(0, index=self.data.index)
        self.signals[short_ma > long_ma] = 1  # Buy signal
        self.signals[short_ma < long_ma] = -1  # Sell signal

        return self.signals

    def execute(self):
        """
        Executes trading signals and generates orders.
        Returns a list of Order objects.
        """
        orders = []

        # Assuming 'signals' is a Series of trading signals
        for date, signal in self.signals.iteritems():
            if signal == 1:  # Buy signal
                orders.append(Order(date, 'BUY', 'TSLA'))
            elif signal == -1:  # Sell signal
                orders.append(Order(date, 'SELL', 'TSLA'))

        return orders

class Order:
    """
    Represents a trading order.
    """
    def __init__(self, date, action, symbol):
        self.date = date
        self.action = action  # 'BUY' or 'SELL'
        self.symbol = symbol

    def __str__(self):
        return f"Date: {self.date}, Action: {self.action}, Symbol: {self.symbol}"

# Example Usage
# Assuming you have historical data loaded in 'historical_data'
strategy_interface = StrategyInterface(historical_data)

# Analyze data and generate trading signals
signals = strategy_interface.analyze()

# Execute trading signals and generate orders
orders = strategy_interface.execute()

# Print the signals and orders
print("Trading Signals:")
print(signals.head())

print("\nOrders:")
for order in orders:
    print(order)



class StrategyExecutor:
    """
    StrategyExecutor is responsible for running and scaling trading strategies.
    It provides functions to run and scale strategies.
    """
    def __init__(self, strategy):
        self.strategy = strategy

    def run_strategy(self):
        """
        Runs a specific strategy.
        """
        self.strategy.analyze()  # Generate trading signals
        orders = self.strategy.execute()  # Execute signals and generate orders

        # Print orders (you can replace this with actual execution logic)
        for order in orders:
            print(order)

    def scale_strategy(self, factor):
        """
        Scales a strategy based on performance.
        """
        # Placeholder for scaling logic (e.g., adjust parameters, allocate capital, etc.)
        pass

        # Example Usage
        # Assuming you have instantiated StrategyInterface with historical data
        strategy_interface = StrategyInterface(historical_data)
        
        # Instantiate StrategyExecutor with the StrategyInterface
        strategy_executor = StrategyExecutor(strategy_interface)
        
        # Run the strategy
        strategy_executor.run_strategy()

# 10. Continuous Monitoring
def continuous_monitoring():
    """
    Continuously monitors the current state of orders, positions, and account details.
    """
    while True:
        open_orders = BrokerIntegration.query_open_orders()
        current_positions = BrokerIntegration.query_positions()
        account_details = BrokerIntegration.query_account_details()

        # Use this information for continuous monitoring
        print("Open Orders:", open_orders)
        print("Current Positions:", current_positions)
        print("Account Details:", account_details)

        # Sleep for a specified interval (e.g., 60 seconds) before checking again
        time.sleep(60)

# Example Usage
data_retrieval = DataRetrieval(provider="Interactive Brokers")
data_storage = DataStorage(data_path="/path/to/data")
data_processing = DataProcessing(data=pd.DataFrame())

# ... (Instantiate other modules as needed)

# Continuous Monitoring (runs indefinitely until manually stopped)
continuous_monitoring()