AI-Driven-FinTech

AI-Driven-FinTech is a comprehensive collection of AI-driven financial technology solutions. This repository explores the application of artificial intelligence and machine learning to various aspects of finance, including algorithmic trading strategies, fraud detection, and risk assessment models.

Key Features

• Algorithmic Trading: Implementations of various AI-powered trading strategies (e.g., reinforcement learning, time-series forecasting).
• Fraud Detection: Machine learning models for identifying fraudulent transactions in real-time.
• Risk Assessment: AI-based tools for evaluating and managing financial risks.
• Portfolio Optimization: Algorithms for constructing and optimizing investment portfolios.
• Market Prediction: Models for forecasting market trends and asset prices.

Getting Started

Prerequisites

• Python 3.8+
• pandas, numpy, scikit-learn
• tensorflow or pytorch
• yfinance (for market data)

Installation

git clone https://github.com/FunctionFlow1/AI-Driven-FinTech.git
cd AI-Driven-FinTech
pip install -r requirements.txt

Usage Example (Algorithmic Trading - Simple Moving Average Crossover)

import pandas as pd
import yfinance as yf

def sma_crossover_strategy(ticker, start_date, end_date, short_window=40, long_window=100):
    data = yf.download(ticker, start=start_date, end=end_date)
    data["SMA_Short"] = data["Close"].rolling(window=short_window).mean()
    data["SMA_Long"] = data["Close"].rolling(window=long_window).mean()
    data["Signal"] = 0
    data["Signal"][short_window:] = np.where(data["SMA_Short"][short_window:] > data["SMA_Long"][short_window:], 1, 0)
    data["Position"] = data["Signal"].diff()
    
    # Backtesting logic (simplified)
    initial_capital = 100000.0
    positions = pd.DataFrame(index=data.index).fillna(0.0)
    positions[ticker] = 100 * data["Signal"]
    portfolio = positions.multiply(data["Adj Close"], axis=0)
    pos_diff = positions.diff()
    portfolio["Holdings"] = (positions.multiply(data["Adj Close"], axis=0)).sum(axis=1)
    portfolio["Cash"] = initial_capital - (pos_diff.multiply(data["Adj Close"], axis=0)).sum(axis=1).cumsum()
    portfolio["Total"] = portfolio["Cash"] + portfolio["Holdings"]
    portfolio["Returns"] = portfolio["Total"].pct_change()
    
    print(f"--- {ticker} Trading Strategy Results ---")
    print(portfolio.tail())
    return portfolio

if __name__ == "__main__":
    # Example: Apply SMA Crossover Strategy to Apple Stock
    apple_portfolio = sma_crossover_strategy("AAPL", "2020-01-01", "2023-01-01")
    # You can further analyze apple_portfolio for performance metrics

    # Placeholder for fraud detection model
    # def train_fraud_detection_model():
    #     # Load transaction data
    #     # Preprocess data
    #     # Train a classification model (e.g., RandomForest, IsolationForest)
    #     print("Training fraud detection model...")
    #     pass

    # train_fraud_detection_model()

Contributing

We welcome contributions from the community! Please read our Contributing Guidelines for more information.

License

AI-Driven-FinTech is released under the MIT License.