ESG-Integrated Quantitative Trading Strategy Backtesting Data: MarketSenseAI Framework with FAME Project Integration

This dataset contains backtesting results and research data from the MarketSense AI quantitative trading framework, specifically focusing on ESG (Environmental, Social, and Governance) integrated investment strategies. The dataset is part of the FAME (Federated Decentralized Trusted Data Marketplace for Embedded Finance) Horizon Europe project (GA:01092639) and includes: Components: Complete Pickle File: Historical daily price data, MarketSense scores, ESG scores, and backtested portfolio trades for the entire experimental period Research Actions: Four comprehensive research studies (September 2025) testing ESG-integrated strategies on SPX100 and broader market datasets Presentation Materials: FAME ESG project documentation and presentation assets Key Research Findings: ESG Strategy Performance: MS020 ESG strategy achieved 61.1% total return (19.5% CAGR) with 1.64 Sharpe ratio and 13.4% maximum drawdown on SPX100 dataset ESG Integration Impact: ESG constraints (λ_esg=1.0) showed performance-neutral to mildly beneficial effects on downside risk without meaningful performance degradation Strategy Optimization: Best configuration identified with buy_signals=1and2, vol_cap=0.2, max_weight=0.1, and monthly rebalancing Technical Specifications: Datasets: S&P100 and comprehensive ticker universe with ESG scoring Time Period: 2022-12-31 to 2025-09-05 (primary analysis) Strategy: MS020_PortSharpeESGPenalizedStrategy with ESG penalty integration Metrics: Total return, Sharpe ratio, volatility, maximum drawdown, Sortino ratio, win rate, profit factor Parameters: Volatility cap, position sizing, lookback periods, ESG penalty weights   ESG Data Assets - Dataset Description These datasets contain ESG (Environmental, Social, and Governance) financial data for S&P 100 companies with the following components: 1. Stock Price Data (Core Tickers) Time Period: 2015-2025 (long-term)  Coverage: S&P 100 companies (100 major US stocks) Data: Daily closing prices for stocks like AAPL, MSFT, GOOGL, etc. Format: Time series with ~1,400+ trading days (2015-2025) and ~600 days (2022-2025) 2. ESG Scores & Metrics ESG Overall Scores: Composite ESG ratings (0-100 scale) Component Scores: Separate Environmental (E), Social (S), and Governance (G) scores Adjusted Scores: ESG scores adjusted based on sector average ESG score Sector Analysis: ESG performance by industry sectors 3. MarketSenseAI Data MarketSense Daily: Daily market signals (-2,-1, 0, 1, 2) MarketSense Rankings: Relative sentiment rankings of companies in [-10, 10] Coverage: 2022-2025 (Sept.) Key Characteristics: Time Series: Daily frequency data from 2022-2025 (605 trading days) Universe: S&P 100 companies (major US large-cap stocks) ESG Focus: Comprehensive sustainability and governance metrics Financial Integration: Combines traditional stock prices with ESG factors Use Case: ESG investing, sustainable finance research, and ESG-aware portfolio construction This dataset is ideal for ESG investing strategies, sustainable finance research, and quantitative analysis that combines traditional financial metrics with environmental, social, and governance factors.