Source Codes for "Contrarian Trading Strategies in Python"

Comparative study between statistical and machine learning based strategies for high frequency trading of assets

Python implementation of a Contrarian Trading Strategy to backtest market reversal approaches using historical stock data. Evaluate performance metrics such as profit, drawdown, win rate, and Sharpe ratio. Built with yfinance for data fetching and numpy and pandas for data analysis.

Prediction market trading bot with contrarian backtest strategy

Follow the momentum or not: Evaluation of Investment Strategies

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JavaFX application that connects to Bitso cryptocurrency exchange, lists the top bids, asks and trades and simulates a contrarian trading strategy.

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# Backtesting of Contrarian & Momentum Trading Strategies

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a contrarian trading strategy on BTC-USDT perpetual futures

Contrarian trading strategy on HSI using social media sentiment + technical filters

Contrarian & Small Market Capital Quantitative Trading Strategy Research on Chinese A-Shares.

Trading sentiment analysis using Fear & Greed Index to uncover contrarian strategies from 200K+ trades

a R language that simulate stock trading algorithm strategy(such as bbands tend folllowing strategy, rsi contrarian strategy, big spender strategy and so on)

Data science analysis of cryptocurrency trades vs market sentiment to identify profitable trading patterns and develop contrarian strategies.

A quantitative analysis pipeline that uses FinBERT to analyze news sentiment for Nasdaq (QQQ), validates causality via Granger test, and backtests a contrarian trading strategy based on sentiment signals.

Trader Behavior Insights analyzes Bitcoin trading data from Hyperliquid against Fear & Greed Index to uncover hidden patterns in trader performance. Identifies contrarian traders, measures consistency across market sentiments, and delivers actionable insights for Web3 trading strategies through advanced data science.

Merged Hyperliquid trader data with Bitcoin Fear/Greed sentiment to uncover hidden patterns. Analyzed leverage, win rates, and trade direction. Isolated the top 5% 'Smart Money' to identify profitable contrarian behaviors, delivering actionable, data-driven insights for optimized Web3 algorithmic trading strategies.

TCF, or The Contrarian Fund, is a proposed trading strategy that is based in mean reversion. The following code is used to algorithmically determine the portfolio composition, on an ongoing basis. This was developed for an Investment Management (BU473) course at Wilfrid Laurier University.