Complete Algo Trading on Fyers API using Python

Automate the Fyers API login with selenium in Python, handling client id, otp, and pin to obtain an access token, with daily checks and chrome driver setup.

Fetch live tick data via WebSockets, subscribe to market symbols, and handle on open, on message, on close, and on error to stream and extract lltp data.

Learn the fundamentals of technical indicators, how to code them in Python, and use them in trading strategies with MacD, RSI, Bollinger bands, ATR, ADX, and Supertrend.

Learn how Bollinger bands use a 20-day moving average and two standard deviations to measure volatility, signaling overbought and oversold conditions, with Google Sheets and Python calculations using close prices.

Average True Range (ATR) measures volatility by averaging the true range—the maximum among today’s high minus low, high minus yesterday’s close, and low minus yesterday’s close—over 14 days.

Compute RSI from daily gains and losses using a 14-day average with a loop, handling NaNs and setting date as the index, then plot close price and RSI.

visualize renko charts with atr-based brick sizing using mplfinance, processing minute data, computing atr, and color-coding bricks in green or red.

Learn to compute ADX values in Google Sheets by calculating true range, DM plus and DM minus, and smoothing dx to derive ADX, with 14 bars for robust directional indicators.

Explore price action by analyzing recent live-market data and candlestick patterns like doji, hammer, hangman, and marubozu to predict near-term moves using Python.

Learn how support and resistance shape price action as price floors and ceilings where buyers and sellers meet, with breakouts and false breakouts creating potential trading opportunities, treated as zones.

Define your objective and risk, choose a market, gather data, and develop a strategy using technical, fundamental, or machine learning methods. Backtest, optimize, and implement in live trading, refining continually.

Explore vectorized backtesting of a simple moving average strategy using 50 and 200 day SMA, computing long and short positions, strategy versus buy-and-hold returns, and limitations.

Explore optimizing a moving average cross strategy by tuning short and long EMA periods, backtesting across 675 combinations, and identifying best and worst parameter sets relative to buy-and-hold.

Expand the backtester with buy and sell functions, balance and net asset value tracking, and position valuation, using data access and indicators like EMA short/long for iterative backtesting.

Close open positions at the last bar, compute and print performance from balance changes, and illustrate iteration techniques (for loops, iterrows, iloc) for backtesting.

Get an overview of a live trading bot built with the Fyers API, featuring real-time data, order placement, and indicators like ema, supertrend, and pivot points.