Backtesting Crypto Trading Strategies with Python & C++ 2021

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Backtesting Crypto Trading Strategies with Python & C++ 2021 [TutsNode.com] - Backtesting Crypto Trading Strategies with Python & C++ 2021 6. High-performance Backtesting with C++
  • 6. Resample the 1-minute Candlesticks.mp4 (154.4 MB)
  • 6. Resample the 1-minute Candlesticks.srt (28.2 KB)
  • 5. Get the HDF5 Data.srt (21.5 KB)
  • 11. Load Your C++ Library Into Python.srt (20.0 KB)
  • 10. Parabolic SAR Coding.srt (17.5 KB)
  • 4. Open and Close an HDF5 File With the C library.srt (16.6 KB)
  • 7. SMA Cross-over Strategy Class.srt (12.3 KB)
  • 1. Set Up the C++ Coding Environment (Windows users).srt (11.3 KB)
  • 8. SMA Cross-over Strategy Execute the Backtest.srt (10.8 KB)
  • 2. Set Up the C++ Coding Environment (Mac OS users).srt (8.2 KB)
  • 3. Run Your First C++ Program.srt (6.9 KB)
  • 9. Parabolic SAR Trading Strategy.srt (4.8 KB)
  • 1.1 cmake_kit.json (0.3 KB)
  • 11. Load Your C++ Library Into Python.mp4 (125.3 MB)
  • 5. Get the HDF5 Data.mp4 (113.4 MB)
  • 2. Set Up the C++ Coding Environment (Mac OS users).mp4 (96.7 MB)
  • 10. Parabolic SAR Coding.mp4 (87.6 MB)
  • 1. Set Up the C++ Coding Environment (Windows users).mp4 (74.7 MB)
  • 7. SMA Cross-over Strategy Class.mp4 (73.1 MB)
  • 4. Open and Close an HDF5 File With the C library.mp4 (65.9 MB)
  • 8. SMA Cross-over Strategy Execute the Backtest.mp4 (61.2 MB)
  • 3. Run Your First C++ Program.mp4 (28.4 MB)
  • 9. Parabolic SAR Trading Strategy.mp4 (17.0 MB)
7. Backtesting Optimization
  • 1. Introduction to Optimization and Genetic Algorithms.srt (21.5 KB)
  • 7. Generate the Offspring Population.srt (19.6 KB)
  • 3. Generate the Initial Population.srt (16.7 KB)
  • 10. Implement the NSGA-2 Process.srt (16.5 KB)
  • 5. Non-Dominated Sorting.srt (15.3 KB)
  • 2. Prepare the Optimizer Module.srt (9.0 KB)
  • 6. Crowding Distance Calculation.srt (8.6 KB)
  • 9. Random Parameter Constraints.srt (6.3 KB)
  • 8. Create the Next Generation.srt (5.6 KB)
  • 4. Evaluate the Population.srt (4.8 KB)
  • 7. Generate the Offspring Population.mp4 (107.5 MB)
  • 10. Implement the NSGA-2 Process.mp4 (90.2 MB)
  • 5. Non-Dominated Sorting.mp4 (83.2 MB)
  • 3. Generate the Initial Population.mp4 (77.7 MB)
  • 1. Introduction to Optimization and Genetic Algorithms.mp4 (71.9 MB)
  • 2. Prepare the Optimizer Module.mp4 (56.8 MB)
  • 6. Crowding Distance Calculation.mp4 (50.7 MB)
  • 4. Evaluate the Population.mp4 (43.6 MB)
  • 9. Random Parameter Constraints.mp4 (36.4 MB)
  • 8. Create the Next Generation.mp4 (26.8 MB)
5. Strategy Backtesting With Python
  • 8. Support & Resistance Coding Identify the Price Levels.srt (20.2 KB)
  • 6. Ichimoku Coding.srt (18.6 KB)
  • 4. On-Balance Volume Coding.srt (16.2 KB)
  • 9. Support & Resistance Coding Check the Breakouts.srt (14.9 KB)
  • 1. Introduction to Backtesting With Python.srt (13.3 KB)
  • 11. Dynamically Input Your Parameters.srt (7.8 KB)
  • 5. Ichimoku Trading Strategy.srt (6.9 KB)
  • 2. On-Balance Volume Trading Strategy.srt (6.8 KB)
  • 3. Backtesting Interface.srt (6.8 KB)
  • 10. Support & Resistance Coding Open Close Positions.srt (6.7 KB)
  • 7. Support & Resistance Trading Strategy.srt (6.2 KB)
  • 13. Performance Indicator the Maximum Drawdown.srt (5.1 KB)
  • 12. Speed Up Your Backtest With Numpy Arrays.srt (4.9 KB)
  • 4. On-Balance Volume Coding.mp4 (89.4 MB)
  • 6. Ichimoku Coding.mp4 (86.0 MB)
  • 8. Support & Resistance Coding Identify the Price Levels.mp4 (76.6 MB)
  • 9. Support & Resistance Coding Check the Breakouts.mp4 (74.9 MB)
  • 1. Introduction to Backtesting With Python.mp4 (48.0 MB)
  • 11. Dynamically Input Your Parameters.mp4 (47.3 MB)
  • 3. Backtesting Interface.mp4 (38.2 MB)
  • 10. Support & Resistance Coding Open Close Positions.mp4 (33.8 MB)
  • 13. Performance Indicator the Maximum Drawdown.mp4 (33.2 MB)
  • 12. Speed Up Your Backtest With Numpy Arrays.mp4 (31.0 MB)
  • 2. On-Balance Volume Trading Strategy.mp4 (25.5 MB)
  • 5. Ichimoku Trading Strategy.mp4 (24.7 MB)
  • 7. Support & Resistance Trading Strategy.mp4 (22.3 MB)
2. Setting Up the Coding Environment
  • 1. Install Python 3.srt (2.1 KB)
  • 3. Source Code for Each Lecture.html (0.4 KB)
  • 2. Install and Configure PyCharm.srt (6.3 KB)
  • 3.1 source_code.zip (662.8 KB)
  • 2. Install and Configure PyCharm.mp4 (21.9 MB)
  • 1. Install Python 3.mp4 (10.5 MB)
3. Market Data Collection
  • 7. Collect the Full Price History of a Symbol (Part 1).srt (17.7 KB)
  • 6. Request Historical Data From Any Crypto Exchange.srt (13.4 KB)
  • 3. Create the Binance Client Class.srt (11.8 KB)
  • 8. Collect the Full Price History of a Symbol (Part 2).srt (11.5 KB)
  • 5. Request Historical Data From Binance.srt (10.4 KB)
  • 4. Request the List of Symbols.srt (7.4 KB)
  • 2. Create an Entry Point Logger Exchange Class.srt (6.8 KB)
  • 1. What is an API and How to Use it.srt (5.2 KB)
  • 6. Request Historical Data From Any Crypto Exchange.mp4 (61.5 MB)
  • 7. Collect the Full Price History of a Symbol (Part 1).mp4 (54.0 MB)
  • 8. Collect the Full Price History of a Symbol (Part 2).mp4 (43.5 MB)
  • 5. Request Historical Data From Binance.mp4 (41.0 MB)
  • 3. Create the Binance Client Class.mp4 (34.2 MB)
  • 4. Request the List of Symbols.mp4 (29.0 MB)
  • 1. What is an API and How to Use it.mp4 (19.5 MB)
  • 2. Create an Entry Point Logger Exchange Class.mp4 (19.1 MB)
4. Storing the Market Data to an HDF5 Database
  • 5. Fetch Data From a Dataset.srt (13.2 KB)
  • 1. What Is HDF5 and Why Use it.srt (12.2 KB)
  • 4. Get the MinMax Timestamp of the Dataset.srt (9.3 KB)
  • 6. Convert 1-minute Candlesticks to any Timeframe.srt (7.9 KB)
  • 3. Insert Data to a Dataset.srt (6.6 KB)
  • 2. Create an HDF5 Dataset With h5py.srt (4.0 KB)
  • 5. Fetch Data From a Dataset.mp4 (69.4 MB)
  • 4. Get the MinM

Description


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



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Backtesting Crypto Trading Strategies with Python & C++ 2021

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2.8 GB
seeders:21
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Backtesting Crypto Trading Strategies with Python & C++ 2021

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