| 0 | 8.5 KB | ||
| 1. Install Python 3.mp4 | 10.5 MB | ||
| 1. Install Python 3.srt | 2.1 KB | ||
| 1 | 10.6 KB | ||
| 1. Introduction to Backtesting With Python.mp4 | 48 MB | ||
| 1. Introduction to Backtesting With Python.srt | 13.3 KB | ||
| 1. Introduction to Optimization and Genetic Algorithms.mp4 | 71.9 MB | ||
| 1. Introduction to Optimization and Genetic Algorithms.srt | 21.5 KB | ||
| 1. Set Up the C++ Coding Environment (Windows users).mp4 | 74.7 MB | ||
| 1. Set Up the C++ Coding Environment (Windows users).srt | 11.3 KB | ||
| 1. What Is HDF5 and Why Use it.mp4 | 45.6 MB | ||
| 1. What Is HDF5 and Why Use it.srt | 12.2 KB | ||
| 1. What is an API and How to Use it.mp4 | 19.5 MB | ||
| 1. What is an API and How to Use it.srt | 5.2 KB | ||
| 1. Why Is Backtesting Essential.mp4 | 24.7 MB | ||
| 1. Why Is Backtesting Essential.srt | 7.2 KB | ||
| 1.1 cmake_kit.json | 307.2 B | ||
| 10. Implement the NSGA-2 Process.mp4 | 90.2 MB | ||
| 10. Implement the NSGA-2 Process.srt | 16.5 KB | ||
| 10. Parabolic SAR Coding.mp4 | 87.6 MB | ||
| 10. Parabolic SAR Coding.srt | 17.5 KB | ||
| 10. Support & Resistance Coding Open Close Positions.mp4 | 33.8 MB | ||
| 10. Support & Resistance Coding Open Close Positions.srt | 6.7 KB | ||
| 11. Dynamically Input Your Parameters.mp4 | 47.3 MB | ||
| 11. Dynamically Input Your Parameters.srt | 7.8 KB | ||
| 11. Load Your C++ Library Into Python.mp4 | 125.3 MB | ||
| 11. Load Your C++ Library Into Python.srt | 20 KB | ||
| 12. Speed Up Your Backtest With Numpy Arrays.mp4 | 31 MB | ||
| 12. Speed Up Your Backtest With Numpy Arrays.srt | 4.9 KB | ||
| 13. Performance Indicator the Maximum Drawdown.mp4 | 33.2 MB | ||
| 13. Performance Indicator the Maximum Drawdown.srt | 5.1 KB | ||
| 2. Common Mistakes When Backtesting.mp4 | 31.6 MB | ||
| 2. Common Mistakes When Backtesting.srt | 9.1 KB | ||
| 2. Create an Entry Point Logger Exchange Class.mp4 | 19.1 MB | ||
| 2. Create an Entry Point Logger Exchange Class.srt | 6.8 KB | ||
| 2. Create an HDF5 Dataset With h5py.mp4 | 12.9 MB | ||
| 2. Create an HDF5 Dataset With h5py.srt | 4 KB | ||
| 2. Install and Configure PyCharm.mp4 | 21.9 MB | ||
| 2. Install and Configure PyCharm.srt | 6.3 KB | ||
| 2. On-Balance Volume Trading Strategy.mp4 | 25.5 MB | ||
| 2. On-Balance Volume Trading Strategy.srt | 6.8 KB | ||
| 2. Prepare the Optimizer Module.mp4 | 56.8 MB | ||
| 2. Prepare the Optimizer Module.srt | 9 KB | ||
| 2. Set Up the C++ Coding Environment (Mac OS users).mp4 | 96.7 MB | ||
| 2 | 658.9 KB | ||
| 2. Set Up the C++ Coding Environment (Mac OS users).srt | 8.2 KB | ||
| 3 | 500.6 KB | ||
| 3. Backtesting Interface.mp4 | 38.2 MB | ||
| 3. Backtesting Interface.srt | 6.8 KB | ||
| 3. Course Organization.mp4 | 12.2 MB | ||
| 3. Course Organization.srt | 4.3 KB | ||
| 3. Create the Binance Client Class.mp4 | 34.2 MB | ||
| 3. Create the Binance Client Class.srt | 11.8 KB | ||
| 3. Generate the Initial Population.mp4 | 77.7 MB | ||
| 3. Generate the Initial Population.srt | 16.7 KB | ||
| 3. Insert Data to a Dataset.mp4 | 29.1 MB | ||
| 3. Insert Data to a Dataset.srt | 6.6 KB | ||
| 3. Run Your First C++ Program.mp4 | 28.4 MB | ||
| 3. Run Your First C++ Program.srt | 6.9 KB | ||
| 3. Source Code for Each Lecture.html | 409.6 B | ||
| 3.1 source_code.zip | 662.8 KB | ||
| 4. Get the MinMax Timestamp of the Dataset.mp4 | 49 MB | ||
| 4. Get the MinMax Timestamp of the Dataset.srt | 9.3 KB | ||
| 4. Open and Close an HDF5 File With the C library.srt | 16.6 KB | ||
| 4. Request the List of Symbols.mp4 | 29 MB | ||
| 4. Request the List of Symbols.srt | 7.4 KB | ||
| 4 | 311.3 KB | ||
| 4. Evaluate the Population.mp4 | 43.6 MB | ||
| 4. Evaluate the Population.srt | 4.8 KB | ||
| 4. On-Balance Volume Coding.mp4 | 89.4 MB | ||
| 4. On-Balance Volume Coding.srt | 16.2 KB | ||
| 4. Open and Close an HDF5 File With the C library.mp4 | 65.9 MB | ||
| 5. Fetch Data From a Dataset.mp4 | 69.4 MB | ||
| 5. Fetch Data From a Dataset.srt | 13.2 KB | ||
| 5. Get the HDF5 Data.mp4 | 113.4 MB | ||
| 5. Get the HDF5 Data.srt | 21.5 KB | ||
| 5. Ichimoku Trading Strategy.mp4 | 24.7 MB | ||
| 5. Ichimoku Trading Strategy.srt | 6.9 KB | ||
| 5. Non-Dominated Sorting.mp4 | 83.2 MB | ||
| 5. Non-Dominated Sorting.srt | 15.3 KB | ||
| 5. Request Historical Data From Binance.mp4 | 41 MB | ||
| 5 | 864.3 KB | ||
| 5. Request Historical Data From Binance.srt | 10.4 KB | ||
| 6. Convert 1-minute Candlesticks to any Timeframe.mp4 | 33 MB | ||
| 6. Convert 1-minute Candlesticks to any Timeframe.srt | 7.9 KB | ||
| 6. Crowding Distance Calculation.mp4 | 50.7 MB | ||
| 6. Crowding Distance Calculation.srt | 8.6 KB | ||
| 6. Ichimoku Coding.mp4 | 86 MB | ||
| 6. Ichimoku Coding.srt | 18.6 KB | ||
| 6. Request Historical Data From Any Crypto Exchange.srt | 13.4 KB | ||
| 6 | 659.1 KB | ||
| 6. Request Historical Data From Any Crypto Exchange.mp4 | 61.5 MB | ||
| 6. Resample the 1-minute Candlesticks.mp4 | 154.4 MB | ||
| 6. Resample the 1-minute Candlesticks.srt | 28.2 KB | ||
| 7. Collect the Full Price History of a Symbol (Part 1).mp4 | 54 MB | ||
| 7. Collect the Full Price History of a Symbol (Part 1).srt | 17.7 KB | ||
| 7. Generate the Offspring Population.mp4 | 107.5 MB | ||
| 7. Generate the Offspring Population.srt | 19.6 KB | ||
| 7. SMA Cross-over Strategy Class.mp4 | 73.1 MB | ||
| 7. SMA Cross-over Strategy Class.srt | 12.3 KB | ||
| 7. Support & Resistance Trading Strategy.mp4 | 22.3 MB | ||
| 7. Support & Resistance Trading Strategy.srt | 6.2 KB | ||
| 8. Collect the Full Price History of a Symbol (Part 2).mp4 | 43.5 MB | ||
| 8. Collect the Full Price History of a Symbol (Part 2).srt | 11.5 KB | ||
| 8. Create the Next Generation.mp4 | 26.8 MB | ||
| 8. Create the Next Generation.srt | 5.6 KB | ||
| 8. SMA Cross-over Strategy Execute the Backtest.mp4 | 61.2 MB | ||
| 8. SMA Cross-over Strategy Execute the Backtest.srt | 10.8 KB | ||
| 8. Support & Resistance Coding Identify the Price Levels.mp4 | 76.6 MB | ||
| 8. Support & Resistance Coding Identify the Price Levels.srt | 20.2 KB | ||
| 9. Parabolic SAR Trading Strategy.mp4 | 17 MB | ||
| 9. Parabolic SAR Trading Strategy.srt | 4.8 KB | ||
| 9. Random Parameter Constraints.mp4 | 36.4 MB | ||
| 9. Random Parameter Constraints.srt | 6.3 KB | ||
| 9. Support & Resistance Coding Check the Breakouts.mp4 | 74.9 MB | ||
| 9. Support & Resistance Coding Check the Breakouts.srt | 14.9 KB | ||
| TutsNode.com.txt | 102.4 B | ||
| [TGx]Downloaded from torrentgalaxy.to .txt | 614.4 B | ||
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| ▲ 163 total files | |||
Description
Backtest your trading ideas before implementing them in real conditions!
Backtesting is an essential step when elaborating a trading strategy. This course will explain how you can use programming to estimate the potential performance of your strategy and avoid unpleasant surprises in live trading.
By the end of the course, you will be able to build your own backtesting framework and comfortably use all its features.
Collect and store large amounts of market data
Before starting to backtest, you need to have a reliable system that collects, stores and organizes the data. You will learn how to fetch data from any cryptocurrency exchange (Binance, FTX…) and store candlestick data efficiently in a powerful file format: HDF5. Many developers do not yet know about this file format, so you will have the upper hand by learning it!
Get your coding skills to the next level with Python AND C++
Python serves as the ideal programming language for building the main features of your backtesting system. You will also use the Pandas library to calculate technical indicators from scratch and control the output of this calculation with precision.
But that’s not all: Do you want to perform backtesting on a large amount of data with many complex operations? This requires a lot of computing power, and this is where C++ coding can be incredibly useful. You will be surprised to discover that C++ is not as scary as it may seem.
Have a scientific approach to your backtesting: use an optimization algorithm!
This course is ambitious, and it addresses real-world problems: you’ll want to find parameters for your strategy that will maximize its performance. To help you with this task, you will learn how to write an optimization algorithm from the Genetic Algorithm family: NSGA-2. When it comes to backtesting, this approach is unique, and you won’t find it anywhere else.
Most of the content of this course can be applied to traditional markets like the stock market.
Disclaimer: This course is not investment advice. The trading strategies are presented as examples.
Who this course is for:
Traders who wish to backtest their strategies efficiently
Crypto exchange users who want to collect market data and store it
Developers who want to combine Python & C++
Anyone interested in multi-objective optimization with Genetic Algorithms
Requirements
Basic Python knowledge (know what a class/object is, dictionaries, lists, functions, loops, etc.)
Basic knowledge about trading (what candlesticks are, Long/Short…)
Last Updated 9/2021
| torrent name | size | uploader | age | seed | leech |
|---|---|---|---|---|---|
|
Udemy - Backtesting Crypto Trading Strategies with Python & C + + 2021 Posted by
freecoursewb in Other
|
2.8 GB | freecoursewb | 4 years | 0 | 0 |
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