Master Algorithmic Trading in the Indian Stock Market

This course section teaches candlestick patterns, trend lines, reversal and continuation patterns, and momentum indicators, with examples of hammer, bullish engulfing, shooting star, and bearish engulfing patterns.

Discover how trend lines map price direction by connecting price pivot points, revealing support, resistance, and chart patterns, with breakouts signaling potential trend reversals.

Study reversal and continuation chart patterns, including head and shoulders and inverted head and shoulders, double tops and bottoms, flags, and pennants, with focus on trend lines and breakouts.

Gauge price and volume momentum to identify trend direction, then use sma/ema, relative strength index, and stochastic oscillator to spot entry points and overbought or oversold conditions.

Identify chart patterns and candlestick signals to forecast price moves across time frames, from flag and pennant patterns to head-and-shoulders, double tops and bottoms, and bullish engulfing and hammer candles.

Build a trading system that prioritizes long-term safety, money management, and position sizing through backtesting. See how a system reduces emotional impact and distinguishes it from a strategy.

Balance win rate and risk-to-reward ratio to drive profitability in algorithmic trading in the Indian stock market, using examples like 60% win rate and ratios 1:2 or 1:3.5.

Drawdowns measure the decline from the highest to the lowest point as a percentage, illustrating risk and recovery needs; a 10% drawdown from 1000 requires 11.11% gain to break even.

Explore trading psychology to manage fear and emotions, think in probabilities, and build self-discipline for consistency. Learn how automatic systems reduce bias, enforce rules, and prevent common trading mistakes.

Master money management and position sizing in the Indian stock market by applying risk management, diversification, and the 2% rule to protect capital and size trades with stop-loss calculations.

Evaluate trading strategies by backtesting against historical data to assess risk and profitability, with longer periods like five years increasing reliability for future automated trading.

Install python via anaconda and verify with conda list, then set up the ide and start exploring python basics: data types, if else, data structures, loops, functions, modules, and exceptions.

Install Visual Studio Code as the IDE, download and install, then use the file explorer to create a Python file like test.py, write code, and run it.

Explore Python data types and practical coding skills, from integers and floats to strings and booleans, including input, type conversion, f-strings, and basic arithmetic and comparisons.

Learn to write if-else statements in python, using if, elif, and else, with indentation and input, to check conditions such as even or odd numbers and fizzbuzz.

Explore Python data structures—lists, sets, tuples, and dictionaries—covering indexing, slicing, and len. Learn list methods like append and insert; use dictionaries for key-value access, and note tuples are immutable.

Explore Python loops by building while and for loops, iterating through lists and strings, and using break, continue, and range to control flow.

Explore Python functions: define with def, parameters, return values, and call with positional or keyword arguments; master default parameters, scope, and global variable usage.

Explore modules and imports in Python, create and import modules and packages, distinguish standard and external libraries, and use NumPy for numerical arrays via pip installations.

Learn to identify syntax, runtime, and logical errors in Python, and master error handling with try/except, including value errors, name errors, division by zero, and file I/O.

Practice six python programs: check unique numbers with sets, display time every two seconds, print patterns, locate words with find, test primality, and save numbers to a file.

Master Pandas for data manipulation and analysis of trading data, including creating data frames, importing and exporting CSV files, filtering and summarizing, and selecting or dropping columns.

Learn to access APIs using Python and the requests library, perform simple get requests to jsonplaceholder and newsapi endpoints, handle status codes, and parse JSON data for trading insights.

Learn how to set up Pushbullet in Python to send and receive notifications across devices, including creating an API key, installing pushbullet.py, and loading keys with dotenv.

Install and import the Python schedule library, define a task, and schedule it to run every two seconds using run_pending in a loop with time.sleep, for algo trading tasks.

Explore object oriented programming concepts through Python examples, covering class, object, attributes, methods, encapsulation, inheritance, and polymorphism with practical dog and animal illustrations.

Explore how to define and use Python enums to group related constants, access members by value or name, iterate them, and extend with methods for modeling algorithm stages in trading.

Learn how to use documentation and prompt engineering to speed up algo trading projects, with Python libraries like pandas and requests, and practical prompting tips to improve model responses.

Master seven best practices for clean, scalable code: readability, maintainability, and scalability; solve one bug at a time; use virtual environments, a requirements.txt, DRY, and .env security.

Configure Kotak Securities Trade API credentials, generate a non-expiring access token, install KS API client, and set up a .env file for login and two-factor authentication.

Learn a bullish trend following strategy using sma9 and sma21 on a one-minute Reliance chart, entering when sma9 crosses above sma21 and exiting on the reverse cross.

Set up the first trading bot project by creating main.py, defining stock constants, implementing a state manager with enum states, and integrating pushbullet notifications with minute-based scheduling.

Implement login, logout, getToken, and updateState in utils.py to support SMA 9 and SMA 21 based market operations.

Compute SMA9 and SMA21 with getSMA in analysis.py by reading data.csv, converting time, setting a time index, filtering by timeframe, and returning the two SMAs.

Implement a position manager that places orders, exits all open today’s positions, and computes realized and open pnl, using the trading api client, instrument token, and transaction types.

Create a Python logging system with log.py and formatted logs. Capture traceback, convert server time, and link logs to trading actions like placing orders and exiting positions.

Deploys a live trading algorithm and fixes errors through end-to-end debugging, logging, and safe exit of positions, detailing login, data handling, SMA 9 and SMA 21, and PNL checks.

An introduction to statistical arbitrage, showing how to simultaneously long and short correlated securities across exchanges to exploit mean reversion, with entry and exit rules and thresholds for high-frequency trading.

Develop a complete arbitrage trading codebase for the Indian stock market, implementing a two-token NSE and BSE strategy with a minimum difference and dual orders every 15 seconds.

Demonstrates live deployment of an arbitrage algorithm on the Indian stock market, placing NSE and BSE orders based on live price differences and a configurable threshold.

Explore a real-time news based trading strategy using sentiment analysis with Vader and News API to generate buy or sell signals from compound scores.

Develop a complete codebase for a news-based trading project, reusing an arbitrage blueprint to drive live market decisions with sentiment analysis and a 0.5 threshold.

This live demo explains planning a news-based sentiment strategy for algo trading in the Indian stock market, generating a buy signal, opening a buy position, and exiting by day end.

Host your trading bot on Python Anywhere using git and GitHub for version control, push a repo, install requirements in a virtual environment, and schedule automated tasks.