Algorithmic Trading using Angel One's Smartapi

Develop a token lookup utility that maps ticker names to instrument tokens via Angel One's Smart API by loading the exhaustive ticker list and filtering for IQ equity.

Discover what technical indicators are and why traders use them, including lagging and leading indicators, and learn to code these tools in Python for trend analysis and signal confirmation.

Fetch historical five-minute candlestick data for multiple tickers and compute macd using fast 12 and slow 26 moving averages with a 9-point signal, deriving the histogram.

Learn to implement the stochastic indicator using SMA-based percent, compute highest high and lowest low with a 14-period lookback, and generate %K and %D via SMA in Angel One smartapi.

Learn how to fetch the last traded price using the LTB data API, apply it to advanced orders, and adjust stop loss logic accordingly.

Learn how to place GTD (good till triggered) orders with Angel One Smart API, a one-year delivery or margin rule that stays alive until triggered, broker-side not an exchange order.

Compute the expected buy and sell prices using ESP, ask prices, and ask sizes, while tracking cumulative position and size to obtain weighted average entry and exit prices.

Demonstrate live testing of signals and trades in a single-threaded websocket setup, reveal data storage challenges, and introduce multithreading in Python to overcome these limitations for order book analysis.

Backtest trading strategies using Interactive Brokers historical data to mimic real trading conditions, factoring in slippage, costs, and conservative entries before deployment.

Explore intraday kpis for backtesting, including absolute return, win rate, and mean return per trade, plus mean returns for winning and losing trades and maximum consecutive loss.

Develop a backtesting framework for orb strategy using Angel One's Smartapi, processing daily and five-minute intraday data, applying slippage, take profit and stop loss rules, and computing date-wise, ticker-level returns.

Deploy the open range breakout backtest to live trading by adapting to intraday five-minute data, implementing position sizing and order management, and maintaining a high-low price track on chosen tickers.

Identify top gap up and gap down stocks using filtered intraday tickers, narrowing to a daily set for the orb strategy and preparing for its deployment.

execute the orb strategy in live trading, identify gaps, track candle highs and lows for Hindalco, Kotak Bank, and HDFC, with no trades due to lukewarm market and low volume.

Transfer your script folder to an EC2 instance using scp with an identification key and recursive transfer. Edit and run scripts with vim and python, planning automation with crontab.

Identify running processes and monitor crontab jobs using ps and grep, then kill undesired processes by pid, and use screen to maintain a visual interface for Python scripts.