Algo-Trading

• build EDA (Exploratory Data Analysis)
• find Correlations and perform Regression analysis on it.

Stock Market Data Analytics: This involves using data

• historical prices, trading volumes,
• financial statements, news articles, etc. for analytics.

Quant Researchers (or Quantitative Analysts): Professionals who use statistical models to analyze.

Strategies: In this context, a strategy is a set of rules or algorithms like below.

• Mean Reversion: Buying stocks with significant price drop than average expecting to bounce back.
• Trend Following: Buying stocks that are trending upwards, expecting the trend to continue.
• Pairs Trading: Identifying two stocks that are historically correlated. Buy the one which is cheaper.
• Statistical Arbitrage: Exploiting small, temporary price discrepancies between related assets. ???

Dataset:

• Price Data: Open, High, Low, Close prices for each stock at various time intervals (e.g., daily, hourly, minute-by-minute).
• Volume Data: The number of shares traded for each stock at each time interval.
• Financial Data: Company financial statements (balance sheets, income statements, cash flow statements).
• News Data: News articles and sentiment analysis related to the stocks.
• Economic Indicators: Data on inflation, interest rates, GDP, etc.

Backtesting: Applying a trading strategy to historical data and evaluate.

• Tracking the simulated trades: Simulate and Track.

• Calculating performance metrics: The system calculates key performance metrics such as:

  • Total Return: The overall profit or loss generated by the strategy.
  • Sharpe Ratio: A measure of risk-adjusted return (higher is better).
  • Maximum Drawdown: The largest peak-to-trough decline in the strategy's equity curve (a measure of risk).
  • Win Rate: The percentage of trades that are profitable.
  • Profit Factor: The ratio of gross profit to gross loss.

• Strategy Refinement: If the backtesting results are poor (e.g., low returns, high risk), modify the strategy's rules or parameters and re-backtest it. This iterative process continues until the strategy shows promising performance on historical data.

Forecasting Algorithms:

• See https://www.kaggle.com/code/tusharsethi1685/stock-prediction-arima-prophet-lstm
• Statistical ARIMA modeling (from statsmodels.tsa.arima.model import ARIMA)
• Forecasting with Prophet (from prophet import Prophet)
• Forecasting with LSTM (from tensorflow.keras.layers import LSTM, Dense, Dropout)

https://www.kaggle.com/datasets/debashis74017/algo-trading-data-nifty-100-data-with-indicators Note: 4GB zipfile, 2015-2025, 1 min resolution, NIFTY 100 Stocks, Uploaded on 05 Mar 2025