Quant Workflow | Algorithmic Trading Strategy R&D in Python смотреть онлайн

This tutorial presents a step-by-step approach to algorithmic / quant trading research and development, breaking down the process as follows:

1) Develop a hypothesis
2) Assess data requirements
3) Create the dataset
4) Generate factors
5) Calculate strategy returns
6) Evaluate results
7) Run statistical tests e.g. what is P(μ != 0) ? (we'll cover this in future tutorials)

Brought to you by Darwinex: UK FCA Regulated Broker, Asset Manager & Trader Exchange where Traders can legally attract Investor Capital and charge Performance Fees:
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Source code is pre-written for you at each step, shared via a Jupyter Notebook available in the DARWIN API Tutorials GitHub repository:
https://github.com/darwinex/darwin-api-tutorials/tree/master/PYTHON/NOTEBOOKS/12_quant_workflow_algo_trading_darwin_api

HANDS-ON learning with source code examples provided during the tutorial.

All source code available on GitHub:
https://github.com/darwinex/darwin-api-tutorials

Enjoy!

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Topics: #algorithmictrading #quanttrading #backtesting #quantresearch #darwinapi #darwinex #darwininfoapi #darwinquotes #darwinreturns #darwinscores #darwininvestmentattributes

Welcome to the DARWIN API - your algorithmic gateway to the Darwinex Community Dataset - brought to you by Darwinex: https://www.darwinex.com/?utm_source=youtube&utm_medium=video-description-above-fold&utm_content=quant-workflow-algorithmic-trading-darwin-api

** Get the latest source code for DARWIN API Tutorials, troubleshoot your applications, give and get help from fellow algorithmic traders and more, over at the Darwinex Collective Slack Workspace:

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