Big Data Implementations - Quantitative_Research
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Updated
Nov 13, 2017 - R
Big Data Implementations - Quantitative_Research
Open source momentum base trend following systematic trading strategies inspired from top trend following traders (Richard Denis, Olivier Seban and Nick Radge) implemented for various trading platforms as TradingView, cTrader, Multicharts and TradeStation.
Orphic Finance landing webpage
Historical performance of single-sort investment strategies.
Replication data and code for "Strategic Asset Allocation Revisited" published on Substack: https://policytensor.substack.com/p/strategic-asset-allocation-revisited.
Implementation of Model 4 explained in the paper "Trend without Hiccups"
Based on the concepts in "CIMTR" and others, swing trading
An algorithmic trading framework for pydata.
Technical analysis and other functions to construct technical trading rules with Python
Quantitative systematic trading strategy development and backtesting in Julia
PyTrendFollow - systematic futures trading using trend following
Analysis on systematic trading strategies (e.g., trend-following, carry and mean-reversion). The result is regularly updated.
A curated list of insanely awesome libraries, packages and resources for systematic trading. Crypto, Stock, Futures, Options, CFDs, FX, and more | 量化交易 | 量化投资
Datasets, tools and more from Darwinex Labs - Prop Investing Arm & Quant Team @ Darwinex
QuantStart.com - QSTrader backtesting simulation engine.
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