Algorithmic Trading Masterclass

What a broker API covers, how it is structured, and where to find help.

Log in to the API manually and understand how session tokens work.

Generate a secret key, set up Google Authenticator, and let the SDK handle login automatically.

Make a live API call from Python and see real market data returned in seconds.

Feed broker API docs to an LLM and generate working code without reading every page.

Fetch the full master list of stocks, F&O contracts and indices.

Download OHLCV candle data for any symbol and timeframe via REST.

Get live price snapshots and market quotes for any instrument.

What tick data is, how it differs from candles, and why F&O traders need it.

Open a WebSocket connection and stream live ticks for any symbol.

What delta, gamma, theta and vega mean — and how traders use them to manage F&O positions.

Stream live Greeks, order book depth and index data simultaneously via WebSocket.

Pull the full option chain snapshot for any index or stock.

Market, limit and stop orders explained — place your first trade in a forward testing environment.

Why trading data is time-indexed and how Pandas handles DatetimeIndex.

Load OHLCV CSV files into a Pandas DataFrame ready for analysis.

Convert raw timestamp strings into proper datetime objects Pandas can work with.

Detect gaps and missing candles in OHLCV data and understand where they come from.

Fix missing values using forward fill, backward fill and interpolation.

Convert 1-minute candles into 5-minute, 15-minute or hourly candles in one line.

Connect to MySQL, create tables and run basic queries on trading data.

Calculate SMA and use it to identify trend direction in price data.

How EMA weights recent prices more heavily and reacts faster to market changes.

Calculate VWAP and understand why institutions use it as an intraday benchmark.

Measure momentum with RSI and identify overbought and oversold conditions.

Combine two EMAs into a momentum signal with the MACD line, signal line and histogram.

Use standard deviation bands to spot volatility expansions and price extremes.

Measure average true range to set smarter stop losses and size positions by volatility.

Build the Supertrend indicator and use it as a clean trend-following signal.

Track buying and selling pressure through On-Balance Volume to confirm price moves.

The five components of every trading strategy: signal, entry, exit, sizing and risk.

What backtesting is, what it can and cannot tell you, and how to avoid common traps.

Use Pandas operations to backtest a strategy across an entire dataset at once.

Search parameter combinations to find settings that improve strategy performance

Build the core loop structure of an iterative backtesting engine from scratch.

Add entry and exit logic to the backtesting loop with real trade tracking.

Implement position sizing and risk rules inside the backtester.

Generate performance metrics: CAGR, max drawdown, Sharpe ratio and win rate.

Run the complete backtester on a real strategy and interpret the results.

Use the iterative engine to find optimal parameters through systematic search.

Build an ATM short straddle using live Greeks to select strikes and define entry rules.

Backtest on historical options data, then execute the full strategy in forward testing mode.

Build a position that stays market-neutral by continuously balancing delta using live Greeks.

Sell ATM call and put in the last 90 minutes of expiry to capture theta decay as premium collapses.

Place a four-leg options spread in a single basket order to profit from low-volatility, range-bound markets.

Trade implied volatility expansion before events and contraction after — using the live options chain.

Dynamically delta-hedge a long options position to profit from large intraday moves.

Use Nifty and Bank Nifty options with live Greeks as filters for directional momentum trades.

Why market orders are frequently rejected in Indian F&O markets — and how to place limit orders that execute at market speed instead.

Set exact entry and exit prices with limit orders and manage their lifecycle.

Attach stop losses to positions programmatically to cap downside automatically.

Modify price and quantity on live orders without cancelling and re-placing.

Cancel open orders programmatically with proper status checks.

Query open positions, average price and unrealised P&L in real time.

Place multiple legs of an F&O strategy in a single API call using basket order functionality.

Subscribe to live order status updates via WebSocket — no polling required.

Calculate the maximum capital to risk on any single trade using fixed percentage rules.

Set a hard stop that halts all trading once a daily loss threshold is hit.

Size each trade based on account capital, risk tolerance and stop distance.

Cap the number of trades per session to avoid overtrading and slippage buildup.

Combine multiple risk rules into a protection layer that runs before every order.

Add a time delay between trades to prevent rapid-fire order sequences after losses.

Use AI to generate a reusable Risk Manager class that wraps any strategy. Include a live dashboard to visualise risk metrics if possible.

Why polling breaks at speed — and how WebSockets give real-time data with no delays.

Generate buy and sell signals live as each new tick or candle arrives.

Watch multiple instruments simultaneously in a single bot without blocking.

Handle the 9:15 start, mid-session events and automatic square-off at 3:20

Build reconnect logic that recovers WebSocket drops without missing a signal

Catch API errors, log every event to file and make your bot production-safe.