NoShade.Vision

Summarized Videos

Chad-Thackray

Backtesting.py Full course in python

The "Backtesting.py" course in Python is an introduction to backtesting and focuses on the essentials of backtesting. Unlike other backtesting libraries, backtesting.py is lightweight, easy to understand, and has a built-in parameter optimizer and heatmap plotter. It has some limitations, such as not being able to trade multiple assets or allowing fractional shares. The instructor demonstrates how to define a strategy as a class and pre-calculate values for technical indicators. The course includes backtesting with a dataset and how to plot and interact with the data using the browser. It explores the backtesting.py dashboard, strategy optimization, and how to apply constraints while using lambda functions. The speaker also demonstrates how to implement different order types, such as stop loss and take profit orders and initiate short positions. Finally, the video covers how to use the "resample apply" function to enable multi-time frame strategies. The course aims to provide beginners with a strong foundation in backtesting and is a great option for those wanting to get started with backtesting.

Pivot Points Indicator in Python

The video explains the Pivot Point indicator, which helps traders identify potential points of support and resistance using data from a previous bar on a larger time frame. The video demonstrates how to calculate Pivot Points, resistance points, and support points for each individual week, using Bitcoin data from Yahoo Finance. The instructor explains the need to shift columns up by one and then appends columns with Pivot Point data onto the hourly table. The Pivot Point indicator is added to the plot using a scatter graph and markers mode. The Y values used are the Pivot Point column, and the resistance lines are added to the plot. The values are then compared to Trading View to check for accuracy. Overall, the video provides an informative guide on how to add the Pivot Point indicator to a Candlestick chart using Python.

Free Forex Tick Data Dukascopy

The video demonstrates how to access free Forex tick data from Dukascopy, including selecting the currency pair, date range, and volume parameters. For those requiring larger amounts of data, the video recommends using a program called Node and provides guidance on installing and configuring this program. The video also provides tips on analyzing the downloaded data using LibreOffice and pandas and identifying gaps or errors in the data. Overall, the video provides a helpful tutorial on working with Forex tick data from Dukascopy.

Multi threaded Backtesting in Python

The video demonstrates how to use multi-processing in Python to speed up backtesting. The presenter explains how most Python programs are single-threaded but with the multi-processing module, it is possible to run several back tests at once resulting in significant performance improvement. The implementation of multi-processing is straightforward and the presenter shares a sample backtester using the backtesting.py module to run a simple RSI crossover strategy. He illustrates how to run the backtest function in parallel using multi-processing and shows how it is much faster than doing it serially. The presenter advises being cautious when running multi-processing on different platforms and environments and recommends guarding significant code with an if name equals main statement to avoid changes in backtesting results.

Convert TradingView indicators to Python

The video demonstrates how to convert TradingView indicators to Python and provides tips for creating an optimized library of indicators in Python. The speaker advises finding the indicator definition in TradingView's language reference manual or within the indicator menu itself and translating Pine script into Python code. They also show how to create a new function in Python that converts TradingView indicators to Python with optimized performance and resistance to NaNs. Additionally, the video covers using numpy and the ‘jit’ decorator from the ‘numba’ package to optimize indicators for faster execution, and emphasizes the importance of having a library of trusted indicators by comparing their values gathered from Python with those of TradingView for accuracy assessment.

Detecting Price Trends in python Higher Highs, Higher Lows

This video tutorial covers using Python to detect uptrends and downtrends in the closing price of an asset by labeling higher highs and higher lows in the price movement. The process can be automated and used as a filter for other indicators that work best when trending in a particular direction. The section also covers data processing steps for getting candlestick data, trimming down the data frame, and calculating the average true range. The video explains how to use the Savitzky-Golay filter from the scipy module to smooth out price data, how to detect higher highs and higher lows in Python using a while loop and conditional statements, and how to turn the trend detection algorithm into an animation using Python. Additionally, the presenter explains how to use a smooth curve instead of a regular price curve to detect uptrends and downtrends and how to display the animation using HTML5 video and save it as an HTML file.

Visualising your trade entries and exits using Plotly

The video demonstrates various techniques for visualizing trades using Plotly in Python. The presenter shows how to create a candlestick chart, scatter plot, and RSI indicator chart while highlighting trade entry and exit points. They also show how to customize the visualizations by adjusting colors and markers and how to add additional information to the hover data. The video emphasizes the importance of plotting entry and exit signals at the appropriate times rather than where they were executed to avoid confusion and ensure accurate backtesting. Overall, the techniques demonstrated can be used to create custom dashboards without relying on pre-built ones in a backtesting framework.

Liquidation order data from Binance in Python

This video tutorial explores how to use websockets in Python to collect liquidation order data from Binance, including the symbol, whether it is a buy or sell order, liquidation price, and amount liquidated. The code includes four imports for websockets, and creates a filename variable to save the liquidation orders to a CSV file for backtesting. The speaker also covers how to check if the CSV file already exists before writing the header, use async IO for the WebSocket streaming, and catch exceptions to reconnect in case the WebSocket connection drops. The tutorial concludes with a reminder to verify the accuracy and meaning of the data and apply the knowledge to collect data from other exchanges.

Cross Sectional backtests in python

The video covers how to backtest cross-sectional strategies using pure Python without frameworks. They explain data gathering using Y Finance, pandas, and numpy, taking care to avoid any look-ahead bias and ensure consistency between the length of the indicator and data frames. The backtesting process involves iterating over every row in the data frame to program trading logic, selecting the K highest or lowest assets out of the available ones. Subsequent sections cover implementing a function to return indexes of the highest/lowest K values, delaying backtesting until a minimum number of assets are available, consolidating contiguous blocks of trades to calculate fees, and performing cross-sectional backtesting using a for Loop to eliminate a key weakness. The speaker also warns against potential errors with NaN values and survivorship bias and suggests optimizing with pure numpy functions and the just-in-time compiler.

Plotly Dash: Orderbook Visualization / Dashboard

This video tutorial covers the step-by-step process of building a customizable and responsive order book dashboard visualization using Plotly Dash and Python, with live data being streamed from Binance. The presenter provides the necessary code and explains important aspects of Plotly Dash, such as callback functions, data retrieval, deep customization, and aggregating datasets. The video tutorial also discusses how to add drop-down menus to the dashboard to allow users to select different cryptocurrency pairs and change various parameters, such as precision and aggregation levels, and how to add styling to the dashboard to make it more visually pleasing. Additionally, the speaker demonstrates how to create reusable components in Plotly Dash using functions and how to add the mid-price to the visualization with precision adjustments. The overall tutorial is beginner-friendly and provides a solid foundation for building more advanced dashboards for trading needs.