Algorithmic Trading on KiteConnect Platform

Establish a connection to the KiteConnect platform by authenticating with your API key and secret, obtaining a request token, creating a session, and creating a trading object.

Automate the authentication flow to generate a request token and access token for KiteConnect, using api key, api secret, user id, password, and pin to start a trading session.

Automate access token generation on the KiteConnect platform using time-based otp and two-factor authentication, including Google Authenticator setup and API key file integration.

Fetch historical candles via the Kite Connect Python wrapper by obtaining instrument tokens, authenticating with an access token, and calling the historical data api with interval and duration.

Explore fetching historical data over extended periods on the KiteConnect platform by splitting date ranges, handling inception dates, and looping from date to date to exceed per-call limits.

learn to place orders via the KiteConnect python wrapper, including market, limit, stop-loss, and bracket orders, and build a boilerplate market-order function with transaction type and product parameters.

Implement bracket orders to place a primary trade with take-profit and stop-loss limit orders. Configure price, trailing stop loss, and know that triggering one leg cancels the other.

Understand WebSocket streaming for live market data, including subscribing to up to 3000 instruments and three modes: last traded price, quote, and full data, plus storage tradeoffs.

Develop streaming tick data on the kiteconnect platform using gatica. Convert symbols to integer tokens, subscribe to tokens, and process real-time ticks with on_connect and on_text callbacks.

Explore technical indicators, learn to code and implement them in trading strategies, and understand lagging versus leading indicators and the difference between technical and fundamental analysis.

Explore the MACD indicator built from fast and slow moving averages, its MACV and signal lines, and how crossovers indicate bullish or bearish trends while noting potential false positives.

implement macd using KiteConnect APIs by computing 12/26 moving averages on low prices from five-minute hlc data, deriving the macd and signal lines, and exploring ema vs sma differences.

Understand Bollinger bands, formed from a moving average and standard deviation, to gauge volatility as bands widen or narrow. Compare with average range indicators to confirm volatility and inform exits.

Implement atr by calculating true range from high, low, and prior close, then smooth with a rolling or exponential moving average over a 14–20 day window.

Explore the relative strength index, a momentum oscillator that measures price move speed, with 0-200 range, 70/30 overbought/oversold signals, and learn its 14-period calculation in Excel plus Python implementation.

Implement the relative strength index in Python by computing price deltas, separating gains and losses, and applying initial simple averages before exponential moving averages to derive the index.

Explore how the adx quantifies trend strength from 0 to 100, via directional movement and bands indicating weak to strong trends, and implement it in Excel (with Python later).

Learn to implement ADX in Excel by computing true range, directional movement, and smoothing over 14 days, then derive the DI+, DI−, and ADX values.

Demonstrates implementing the adx indicator in python by calculating true range, plus and minus directional movement, and smoothing with a 14-period setup.

Explore the supertrend, a trend-following indicator using ETR based upper and lower bands and a multiplier. It performs best in trending markets and is used with Masoud.

Implement the supertrend in Excel by calculating true range and ADR, building basic and final upper and lower bounds, and handling trend reversals with placeholders.

Use the Stock Trends Library to convert ohlc data into renko bricks, resetting the index and setting brick size to reveal brick colors as trend signals.

Discover how support and resistance shape price action, acting as floor and ceiling as bulls and bears contend for control. Examine chart interpretations, historical prices, and calculating philosophies.

Explore pivot points as a leading indicator that defines support and assistance levels, and learn to compute them from previous period highs, lows, and close using Python.

Implement a python doji detector using api data by computing the average candle size from close and open differences, and flag doji candles within five percent of that average.

Identify hammer candlesticks, where the lower wick is at least twice the body, preferably green, signaling bullish reversal at the end of a downturn; a key candlestick pattern in strategies.

Identify the shooting star as a bearish reversal after an uptrend, featuring a large upper wick at least twice the body and minimal lower wick, with price action context.

Compute the average candle size from the data, then identify candles whose body exceeds two times that average and wick sizes stay under five percent, demonstrated on Asian Paints.

Discover how two-candlestick patterns—bullish and bearish engulfing, and harami cross—signal trend reversals, with guidance on interpretation, confirmation, and a future scanner for signals on KiteConnect.

Explore slope implementation using APIs, applying a regression on candle midpoints and a trend rule based on highs and lows across seven candles to form robust signals.

Explore Python's time module to run automated trading scripts at fixed intervals, using sleep and system time to execute every five seconds, with a Fibonacci number demo.

Build a pattern scanner that analyzes stock charts and emits signals when patterns form near pivot point resistance or support levels, using daily data and intraday candles.

Learn to build a pattern scanner that identifies candle types (dorji, marabous, hanging man, shooting star, harami cross, engulfing patterns) near support and resistance using intraday and daily data.

Design and deploy your algorithmic strategies on the KiteConnect platform by planning entry, exit, and risk controls, with what-if contingencies.

Build a Python implementation of the supertrend strategy, detailing modular functions for stop loss, place order, and trailing stop updates, while tracking signals across three supertrend lines.

Iterate over the top movers using intraday five-minute candles to compute the supertrend, determine trend direction, and place market buy or sell orders with stop-loss based on available capital.

Implement a robust supertrend trading strategy on KiteConnect by handling blank and non-blank data frames, managing positions, adjusting stop losses from supertrend lines, and iterating the strategy over time.

Watch a supertrend strategy demo, validating entry signals and positions at market open. Learn why market orders convert to limit orders in fast moves to prevent slippage and circuit-breaker risk.

Automate intraday square-off on the KiteConnect platform by closing open positions with market orders and canceling pending or open orders before the intraday cutoff, preventing penalties.

Demonstrate square off on the grid by pulling position and order data frames, offsetting an open position with pending orders, and canceling them, while noting authentication to prevent token expiry.

Store streaming data in a SQLite database via Python using escalatory, creating per-ticker tables with timestamp, price, and volume, and insert streaming ticks with unique timestamp handling.

Update renko bricks in real time by streaming price data with KiteConnect, subscribing to tokens via on connect and processing data in on x for strategy integration.

Learn to implement a real-time Renko and MACD trading template by integrating streaming indicators with candle-based signals, using building blocks, MACV crossover, and a main function to orchestrate execution.

Demonstrates real-time renko brick trading on Kite Connect, showing adjustable brick sizes, price rounding to a single decimal, five-minute versus one-hour framing, and live stop-loss orders with trade updates.

Launch and configure an AWS EC2 instance by selecting an OS, choosing a micro instance in Mumbai, adding storage and a key pair, and restricting SSH access to your IP.

Connect to an EC2 instance via browser or terminal, adjust security group inbound rules for your IP, and set up the pem key in Windows Subsystem Linux to enable SSH.

Install ChromeDriver and Chrome browser on EC2 to enable Selenium automation for KiteConnect trading scripts; learn to configure paths, install dependencies, and run Linux-based access token workflows.

Learn to handle Linux file paths in Python for algorithmic trading on KiteConnect by using os.path.join, managing the current working directory, and adapting Chrome driver paths across Windows and Linux.

Automate trading scripts on Linux with cron by creating an executable script and scheduling it with crontab -e using minute, hour, day of month, month, and day of week.

Learn to manage long-running tasks with cron jobs and the screen command, detach and reattach sessions, monitor with ps aux and grep, and safely kill processes on Linux.

Extract option chains algorithmically from Zerodha Kite and Angel One to identify Bank Nifty options by underlying, expiry, strike, and put or call for algorithmic trading.