Advanced strategy development for Crypto with Python

Learn to fetch and clean Binance api candlestick data with python, extracting ohlcv (open, high, low, close, volume) for btc at configurable intervals.

Refine a Python volume profile implementation for crypto trading, computing value area volume, high and low, and the volume point of control to inform decisions.

Identify crypto price breakouts by locating the first bar in the breakout direction after a long bar, then derive the level index from the latest non-same-direction bar within a 24–48 hour window.

Compute the daily range by subtracting the daily low from the daily high, using day boundaries and a daily range function to support breakout entry points in crypto with Python.

Use volume profile to identify peak volume nodes near a level and adjust the level using the closest volume note, with data sliced 24 hours before and 1 hour after.

Visualize trades by plotting a window of 100 hours before and five hours after each trade, highlighting stop-loss and take-profit levels with color-coded candles.

Identify the closest resistance by filtering older-than-current levels and price constraints, select the lowest valid level, and visualize it in a candlestick-like chart that updates in real time.

Create a data frame of trade results and plot the equity curve to evaluate a crypto strategy's metrics. Track starting capital, cumulative profit, profits, losses, and accuracy, including open trades.

Explore how to use linear regression to decide long or short for crypto trades, loading Bitcoin data, plotting with scatter plots, and fitting a least-squares regression line using Escalon.

Backtest a linear regression based crypto strategy that uses slope relative to a slope limit to trigger long or short trades at resistance and support levels.

Test out-of-sample data to validate strategy parameters and observe equity performance for etherial and bitcoin, noting that strong in-sample results may not hold in new markets despite Monte Carlo analyses.

Explore advanced crypto strategy development in Python by optimizing parameters, applying look forward testing with demo data, and implementing a levels finder across crypto pairs for performance evaluation.

Develop a robust data downloader workflow that checks a data folder, fetches new crypto data by timestamp, saves to csv, reads and concatenates updates, and excludes incomplete last rows.

Visualize live crypto data with mplfinance by plotting candlesticks and updating every 10 seconds, while displaying the latest BTC USD price and time and applying level-based supports and resistances.

Modify the crypto strategy by importing an existing notebook, updating level data and parameters, and saving updated levels to the screener for level-based trades.