Algorithmic Trading: Backtest, Optimize & Automate in Python (2021)

[lovewarez]

Algorithmic Trading: Backtest, Optimize & Automate in Python (2021)
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 32 lectures (47m) | Size: 986 MB

Learn How to Use and Manipulate Open Source Code in Python so You can Fully Automate a Cryptocurrency Trading Strategy What you'll learn:
Use Python to Automate your Cryptocurrency Trading
Optimize your Strategy to Find the Best Parameters to Use
Connect to Multiple Cryptocurrency Exchanges
Use Open Source Code Freqtrade
Load Historical Data and Backtest your Strategy
Run the Strategy in Simulation or Live
Be able to work on a Virtual Environment
Communicate with the Strategy through your Phone

Requirements
Some Basic Programming knowledge (Any language)
Basic Cryptocurrency Trading Knowledge

Description
Welcome to Python for Financial Analysis and Algorithmic Trading! Are you interested in how people use Python to conduct rigorous financial analysis and pursue algorithmic trading, then this is the right course for you!

This course will guide you through everything you need to know to use Python for Finance and Algorithmic Trading! We'll start off by learning the fundamentals of Python, and then proceed to learn about the various core libraries used in the Py-Finance Ecosystem, including jupyter, numpy, pandas, matplotlib, statsmodels, zipline, Quantopian, and much more!

Since the public release of Alpaca's commission-free trading API, many developers and tech-savvy people have joined our community slack to discuss various aspects of automated trading. We are excited to see many have already started running algorithms in production, while others are testing their algorithms with our paper trading feature, which allows users to play with our API in a real-time simulation environment.

When we started thinking about a trading API service earlier this year, we were looking at only a small segment of algo trading. However, the more users we talked with, the more we realized there are many use cases for automated trading, particularly when considering different time horizons, tools, and objectives.

Today, as a celebration of our public launch and as a welcome message to our new users, we would like to highlight various automated trading strategies to provide you with ideas and opportunities you can explore for your own needs.

We'll cover the following topics used by financial professionals:

Python Fundamentals

NumPy for High Speed Numerical Processing

Pandas for Efficient Data Analysis

Matplotlib for Data Visualization

Using pandas-datareader and Quandl for data ingestion

Pandas Time Series Analysis Techniques

Stock Returns Analysis

Cumulative Daily Returns

Volatility and Securities Risk

EWMA (Exponentially Weighted Moving Average)

Statsmodels

ETS (Error-Trend-Seasonality)

ARIMA (Auto-regressive Integrated Moving Averages)

Auto Correlation Plots and Partial Auto Correlation Plots

Sharpe Ratio

Portfolio Allocation Optimization

Efficient Frontier and Markowitz Optimization

Types of Funds

Order Books

Short Selling

Capital Asset Pricing Model

Stock Splits and Dividends

Efficient Market Hypothesis

Algorithmic Trading with Quantopian

Futures Trading

Who this course is for
How to use freqtrade (it's an open source code)
Use a Virtual Machine (we provide you one with all the code on it, all you need to do is download it)
Learn How to code any strategy in freqtrade (We show you how to code a strategy and show you a repository with other strategies)
Backtest a strategy so you can see how it would have performed in the past
Optimize a strategy to find the best parameters to get the best reward/risk ratio
Do a walk-forward analysis to see how a strategy would perform with out-of-sample data (to minimize overfitting)
Run the strategy with paper money (Extremely important step, in order to test out your code without risking any real capital)
Run the strategy with real money

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