Zerodha Kite Connect API for Algo Trading

Automate logging into the Zerodha Kite Connect API with Selenium, using API key, secret, user ID, and time-based OTP. Store temp token and save access token securely in text file.

Explore essential Zerodha Kite Connect API functions for algo trading, including login, trades, orders, positions, holdings, margins, last price via LTB and code for multi-symbol data and deeper insights.

Explore kite ticker's web socket streaming to access real time tick data during live markets, with options to subscribe to ltb data, core data, or full data.

Download the full option chain data via Zerodha Kite Connect, filter for call and put options on Nifty, and extract the ten nearest strikes with their last prices.

Explore the basics of technical indicators, how they derive from price, volume and open interest data, how to code them in Python, and how to use them in trading strategies.

Compute the moving average convergence/divergence as the difference between slow and fast moving averages, then apply a nine-day signal line to spot bullish or bearish crossovers.

Explore Bollinger bands to assess volatility using a 20-day simple moving average with upper and lower bands two standard deviations away, signaling overbought and oversold conditions.

Calculate the average true range to measure volatility by computing true range from high, low, and previous close, then apply an exponential moving average over 14 days.

Learn to chart close price and ATR on a shared axis, interpret volatility with ATR and Bollinger bands, and use these complementary indicators to assess trend sustainability in market data.

Explore the relative strength indicator (RSI), a leading momentum oscillator from 0 to 100 that signals oversold below 30 and overbought above 70 using average gains and losses.

Import data and compute daily gains and losses from price changes to prepare the RSI workflow. Compute a 14-day average via a loop, calculate RSI, and visualize price and RSI.

Explore the super trend, a trend following indicator using average true range and a default multiplier of three to create upper and lower bands for intraday trading, signaling reversals.

Learn to compute the super trend in Google Sheets using true range, ATR, EMA, and upper and lower bands. Understand trend shifts and band updates, prep for Python coding.

Calculate the super trend in python using a 7-period range and a 3 multiplier, deriving basic and final upper and lower bands and tracking trend shifts.

Explore renko charts as a noise-filtered, brick-based view of price movement, where bricks form only after specified price moves, with flexible brick size and an unfixed time axis.

Learn to build renko charts in Python by computing brick counts from price changes with brick size, applying green and red colors, and integrating brick data for algo trading.

Learn to visualize renko charts using atr-based brick size with mplfinance, plotting nifty data and color-coding bricks to show trend changes.

Learn how the average directional index (adx) quantifies trend strength, with a standard 14-day period, to signal whether bulls or bears control the market and filter sideways markets.

learn to compute adx in google sheets using true range, positive and negative directional momentum, and 14-bar smoothing to derive di plus, di minus, and the adx.

Learn to compute the ADX and directional indicators in Python using the pandas library, supplying highs, lows, and closes, with a single line of code and a data frame output.

Analyze price action in live markets using recent candlestick data, patterns, and concepts like trend, range, spike pullback, and breakout reversal. Code simple candlestick indicators in Python for algo trading.

Master the basics of candlesticks by examining how each candle represents open, high, low, and close prices, with bodies, wicks, and bullish or bearish patterns.

Explore how support and resistance define price levels where demand and supply push reversals, breakouts, and false breakouts, guiding traders to buy at lows and sell at highs.

Pivot points indicate market trend by averaging yesterday's high, low, and close. Use them to calculate intraday resistance and support levels, including r1, r2, r3 and s1, s2, s3.

Learn to compute pivot points from yesterday's high, low, and close in Python, derive resistance and support levels with rounding to two decimals, and visualize these lines alongside intraday prices.

Learn how doji candles signal indecision, show open and close prices that are the same, explore doji types, and implement Python-based detection for algo trading with live data.

Learn to use Talib and MPL Finance to identify doji candles in Python, create a signal column in a data frame, and visualize and filter doji candles for trading strategies.

Explore hammer candles, a candlestick indicator with a small body and long lower wick, signaling recovery and a potential reversal, with wick at least twice the body.

Detect hammer candles in Python with TA-Lib, update the data frame and signal list when the hammer value hits 100, and plot an interactive chart highlighting the hammer.

Identify a shooting star candle by a lone upper wick more than twice the body and little or no lower wick, signaling a bearish reversal after an uptrend.

Use Python with the clip library and plot to detect shooting star candles from months of five-minute data, using Talib's shooting star function and visualizing them on a candlestick chart.

Identify marubozu candles, candlesticks with a full body and little to no wicks, signaling strong bullish or bearish momentum. Use them to confirm breakouts and prepare for Python-based detection.

Detect marubozu candles with Python by reading data, filtering by date, and computing open, high, low, close to identify green or red marubozu signals using average candle size and wick.

Explore harami candlestick patterns, including bullish and bearish harami and harami cross, with two-candle formations and doji signals, and learn to detect them in Python using talib kdl harami.

Learn the engulfing pattern, the opposite of harami, where a smaller candle is engulfed by a larger one, signaling reversals at downtrends and uptrends, with weeks optional and VIX considerations.

Explore how time series data records values over time using stock market per-minute data with open, high, low, and close, and parse dates to datetime for indexing and filtering.

Read data into pandas dataframes from csv, tsv, and excel, and manage working directories and file paths with os. Learn to read from urls and clipboard, and write to csv.

Convert a date time string to a timestamp in pandas using pd.to_datetime, overwriting the DateTime column to datetime64 and handling errors with ignore or coerce.

Learn how pandas handles missing values by default, identify nan values in data and CSV, replace with np.nan or None using iloc, and count non null values with info method.

Learn to handle nan values in pandas using isna and isnull, dropna with how, axis, and threshold, and replace or fill missing data with mean, forward fill, backfill, or interpolation.

Discover how to resample time series data from tick level to per minute, five minute, or daily intervals using upsampling and downsampling with aggregates such as last and ohlc.

Connect to a MySQL server from Python to explore the training database and the students table. Execute select, insert, update, and delete operations with commit using mysql-connector.

Define objectives, select markets, gather and backtest data to develop profitable algo trading strategies using technical, fundamental, or machine learning methods, then optimize and implement live, with ongoing refinement.

Examine backtesting for algo trading, comparing vectorized and iterative approaches and their limitations, with Python tools like pandas, numpy, and backtrader.

Develop an iterative backtesting class in Python to test trading strategies step by step, using pandas, numpy, and date handling, with reusable EMA parameters, balances, and logs.

Builds an iterative backtester that loads price data from a CSV file, computes log returns and short and long EMAs, and plots price and returns to support step-by-step trading simulations.

Enhance the backtester by implementing buy and sell functions, updating units and balances, and computing current position and net asset value for iterative backtesting.

Close any open positions at the end of the backtest and generate a performance metric from initial to final balance. Learn iteration techniques like range loops, iterrows, and iloc.

Backtest an exponential moving average crossover strategy with short-term and long-term EMAs to signal trades, showing a 9.67% return from 70,000 to 76,770 across 26 trades (costs omitted).

Explore iterative strategy optimization by testing multiple parameter combinations, backtesting performance, and identifying top configurations using range-based trials, product, and data frame analysis.

Demonstrates a macd and supertrend based trading strategy that uses icici direct wrapper to fetch data and place orders, with potential integration to zerodha kite connect for paper trading.

Test your algo trading strategy in the real market with Zerodha Kite Connect API, using MACD and Supertrend to pick top moving stocks and place intraday buy and sell orders.

Run your strategy on a static ip address vps for 24/7 reliability. Connect remotely via Microsoft Remote Desktop from Linux or Windows, using AWS, Google Cloud, or other hosting options.

Automate your Zerodha trading workflow on Windows server with scheduled batch files and Python scripts for login, data download, calculations, and strategy execution, plus a Flask interface on port 5000.

Access your strategy from a browser on a VPS, select instruments, run pre-market checks, and monitor live paper trading with detailed logs and historical data.