Building Technical Indicators in Python

Learn how moving averages smooth price data to reduce noise, comparing simple and exponential moving averages in Python, with slow and fast lines, and video on moving average convergence divergence.

Compute macd by subtracting the 26-day slow moving average from the 12-day fast moving average, then apply a 9-day signal line; crossovers indicate bearish or bullish trends.

Learn how Bollinger bands measure volatility with a 20-day moving average and two standard deviations, signaling overbought or oversold conditions, and implement the calculation in Google Sheets and Python.

Explore how average true range measures volatility by tracking daily high-low movement and past close, compute a 14-day exponential moving average, and compare with Bollinger bands.

Display close prices and ATR values on a shared chart with twin axes to reveal volatility, trend dynamics, and how ATR complements Bollinger bands for market stabilization.

Explore how the relative strength index, a leading momentum oscillator from 0 to 100, signals trend reversals and overbought or oversold conditions using average gains and losses.

Build a python RSI workflow: import data, compute gains and losses from consecutive closes, iteratively update 14-day averages, derive relative strength, calculate RSI, and plot close with RSI.

Demonstrate how the supertrend indicator uses ATR, period, and a multiplier to create upper and lower bands, signaling bullish or bearish trends in intraday markets.

Learn to compute the Supertrend indicator in Google Sheets or Excel using true range, ATR, seven-period EMA, and a three multiplier, with upper and lower bands.

Learn to compute the supertrend indicator in Python using ATR with a range period of 7 and a multiplier of 3, deriving basic and final upper bands and trend shifts.

Explore Renko charts, where bricks form only after a defined price movement, using a brick size, filtering noise and ignoring fixed time intervals to reveal price trends.

Write python code to build renko bricks from current and previous close, using brick size, and color bricks green or red for visualization and algo trading.

visualize renko charts by calculating brick size with ATR, plot using the finance library on matplotlib, and build color-coded bricks from data frames.

Learn how the average directional index (adx) measures trend strength on a 0–100 scale, distinguishing bulls from bears, and uses plus and minus directional indicators to filter sideways.

Calculate true range, positive and negative directional momentum, and smoothed directional indicators with a 14-period window in Google Sheets, laying the groundwork for Python-based analysis.

learn to compute the ADX indicator in Python using a pandas library with highs, lows, and close values, producing a data frame of plus and minus indicators and plotting trends.

Learn price action in the live market using data and candlestick patterns to gauge trend. Identify patterns like doji, hammer, hangman, and marubozu, code them in Python for swing trading.

Explore candlesticks as a graphical representation of price data for a given period, with open, high, low, and close, body and wick variations, and bullish and bearish patterns.

Explore support and resistance as price levels where trends may bounce or reverse, including breakouts and false breakouts, and view these levels as zones to measure for future price action.

Discover how pivot points indicate market trend by averaging yesterday's high, low, and close to signal bullish or bearish sentiment and set intraday support and resistance levels.

Calculate pivot points, resistance, and support in Python from yesterday's high, low, and close, rounding to two decimals, then test against intraday data and compare with Zerodha's graph.

Explore doji candles in candlestick patterns and how open equals close signals indecision. Learn to code doji detection in Python using live tick data for algo trading decisions.

Learn to identify doji candles in price data using Talib library in Python, convert detections into 0/1 signal in a dataframe, and visualize results with finance on a Jupyter notebook.

Learn how hammer candles, a candlestick indicator, signal reversals after declines, with a small body and a long lower wick at least twice the body, indicating recovery from the lows.

Learn to detect hammer candles in python using talib and plotly, update a dataframe with a hammer signal, and visualize results interactively with a hammer trace.

Explore the shooting star candle with a long upper wick and little or no lower wick, signaling a bearish reversal after an uptrend; it contrasts with inverted hammer.

Learn to detect shooting star candles in Python using the Talib library and Plotly to display candlesticks, generate star signals, and analyze uptrend versus downtrend contexts.

Identify marubozu candles, full-bodied with little to no shadows that signal strong bullish or bearish momentum, used to confirm breakouts, with Python detection covered in the next video.

Develop marubozu detection in Python by filtering data, computing open, high, low, close, upper and lower wicks, and visualizing green and red signals with Plotly.

Learn harami and harami cross patterns in two-candle candle formations, including bullish, bearish, doji variants. See how Python detects them with talib on five-minute data and plots.

Master the engulfing pattern, a bullish reversal at the bottom of a downtrend or a bearish reversal at the end of an uptrend, using candle bodies and average size.

Develop a live pattern scanner to detect candlestick patterns across multiple stocks, using slope and trend lines to determine market direction and trigger alerts.

Explore using Talib to compute a Hilbert transform-based trend line on close price, estimate slope in degrees to identify bullish or bearish trends, and examine candle patterns including hammer candles.

Build a pattern scanner in Python that detects popular candlestick patterns using Talib, classifies candles as bullish or bearish from slope, and visualizes patterns under the low price.

Explore pattern scanning for candlestick patterns on five minute action, calculating pivot points and resistance levels, and using ICICI Direct or Zerodha data to fetch prices and build trading strategies.

Develop a pattern scanner that loops through symbols, fetches five-minute candles, computes pivot points, resistance and support, and identifies candlestick patterns near key lines.

Define your algo trading objective, select a market, and gather clean data to develop and backtest a profitable strategy. Optimize, implement in live trading, and continually refine as conditions change.

Explore vectorized backtesting of a simple moving average strategy using 50-day and 200-day averages to generate long and short signals, and compare returns with buy-and-hold while noting vectorization limits.

Optimize a moving average strategy in Python by testing multiple short and long EMA periods, backtesting 675 combinations, and comparing to buy-and-hold results to identify the best performing parameters.

Demonstrates a demo python strategy using macd and supertrend to generate long or short signals on top moving stocks, with data fetching, icici direct wrapper, selenium login, and position management.

Test strategy in the real market using top moving stocks with MACD and Supertrend signals, automate intraday trades via Zerodha with Python, and emphasize testing and fine-tuning before real money.