Complete Algo Trading on Fyers API using Python

Set up a totp key for fyers and automate login with PyOTP to generate time-based one-time passwords for api access.

Master manual login to the Fyers API by using app id, secret, and redirect url to obtain an authentication code and access token, then verify via get profile.

Learn how to use the Python selenium library to automate browser actions, install via pip, and control the Chrome web driver to interact with login pages and page elements.

Explore web scraping basics by building a simple HTML page, identifying elements with XPath, and using selenium to read text and click a button, demonstrating browser automation.

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.

Explore common Fyers API functions with Python, including client profile, funds, and holdings. Retrieve quotes and market depth to access last price, open, high, low, and depth data.

Identify instrument symbols and types from CSV symbol files, map the fields, and convert expiry dates from Unix to IST to filter nearest expiry banknifty options for orders.

Fetch historical data from the Firebase API by building a symbol-based query with index, resolution, and date range, then parse OHLC candles and adjust timestamps to Indian time.

Learn to place orders on the Fyers API with Python, including market, limit, stop-loss, and bracket orders with take profit for intraday and CNC, plus modify, cancel, and exit positions.

Fetch the order book and inspect orders to view status, traded price, and symbol details; iterate by order ID to retrieve order data, and explore positions and the trade book.

Explore how the Fyers WebSockets API delivers live tick data, order updates, and alerts through web streaming, using Python, Node, and JavaScript for real-time trading.

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 to smooth price data with simple and exponential moving averages, using Python to compute SMA and EMA from close prices, compare their responsiveness, and visualize trends.

Compute the MACD by subtracting 26-day slow from the 12-day fast moving average, then use a 9-day signal line to spot bullish and bearish crossovers signaling trend strength and reversals.

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.

Demonstrates plotting close prices and ATR on a shared chart, explaining how ATR and Bollinger bands reveal volatility and trend sustainability, and how to interpret their signals together.

Learn the relative strength indicator (rsi), a 0–100 momentum oscillator identifying oversold and overbought conditions. Apply average gain and loss with 14-day smoothing to compute rsi via 100 - 100/(1+rs).

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.

Explore the supertrend indicator, using atr-based upper and lower bands, a period for atr calculation, and a multiplier of 3 to spot bullish or bearish trend reversals in intraday markets.

Explore how to compute the supertrend indicator in Google Sheets or Excel using true range, seven-period ATR with EMA, and a multiplier of three, including upper and lower bands.

Compute the supertrend in Python using ATR with a seven period range and a multiplier of three, and apply basic and final upper bound to identify shifts.

Learn how Renko charts visualize price movement through bricks, not fixed time intervals, using brick sizes (fixed price, percentage, or ATR) and close prices.

Write python code to generate Renko bricks for algo trading and visualize historical data, calculating bricks from price moves using brick size and color changes.

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

Learn how ADX measures trend strength to show whether bulls or bears control the market, with a 0–100 scale and a 25 threshold, and its use with directional indicators.

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.

Compute the adx indicator and directional indicators in Python using pandas, with highs, lows, and close inputs; compare results with Google Sheets and visualize the adx trend.

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.

Explains candlesticks as candles that represent OHLC data for a given period, detailing open, high, low, close, body size, and wicks, and distinguishes bullish and bearish candles.

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.

Explore pivot points as a key technical indicator that signals market trend using yesterday's high, low, and close, and derive intraday support and resistance lines.

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.

Discover backtesting for algo trading on historical data using Python libraries like backtrader, zipline, vector, and pandas, with vectorized and iterative approaches for fast screening and avoiding look-ahead bias.

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.

Build an iterative backtesting class in Python to test trading strategies step by step, tracking trades, balances, and data using pandas and numpy.

Enhance the backtester by loading CSV data, converting dates, computing log returns and short/long EMAs, and adding plotting to visualize price, EMAs, and returns for iterative backtesting.

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.

Backtest an exponential moving average crossover strategy using short-term and long-term EMAs to generate buy and sell signals, while tracking balance and trade statistics in an iterative loop.

Explore iterative strategy optimization by backtesting multiple ema parameter combinations, using itertools.product and data frames to identify the top and bottom performers.

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.