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A great backtest can tell a compelling story. But can you trust the data behind it? (full PDF in the comments) At Needlestack Technologies Ltd., backtesting and data quality are at the core of what we do. We continuously challenge our datasets, refine our methodologies and improve our processes, so our clients can build their research on a reliable foundation. Some of our most valuable improvements come from conversations with our clients and the wider quantitative research community. So I put together a list of the 15 resources on backtesting I keep coming back to: papers, white papers and even a podcast on what it really takes to trust a historical result. The full list with links is attached. It includes white papers, academic research and a podcast from: AQR Capital Management · Man Group · Two Sigma · Robeco · Capital Fund Management (CFM) · Amass Insights · validityBase · WorldQuant · State Street · Jane Street alongside foundational academic work by Marcos Lopez de Prado, David Bailey and Campbell Harvey. Across these sources, the same failure modes appear again and again: look-ahead bias, overfitting, poor data quality, regime shifts and out-of-sample tests that were never truly out of sample. In my view, a better backtest does not start with a better equity curve. It starts with cleaner data, sharper questions and the discipline to challenge even your most promising ideas. What has been your biggest challenge when validating historical data or backtesting a strategy? If there is a paper you believe belongs on this list, or a source you think is overrated, I would genuinely welcome your perspective. #Backtesting #QuantitativeFinance #DataQuality #AlternativeData #QuantResearch

New Data Products Dont Have Years of Live History. Thats Why Backtest Validation Matters More. For established data products, years of live history provide valuable evidence of reliability. But for newly launched datasets, that history simply does not exist. This makes rigorous backtesting particularly important. A well constructed historical dataset allows researchers to evaluate signals across a broader universe, different market conditions and longer time periods without waiting years to accumulate live observations. However, the value of any backtest depends on the quality of the validation framework behind it.At Needlestack Technologies, we combine historical testing across a large universe dating back to 2020 with live observations. We use these observations to verify historical snapshots, investigate discrepancies and continuously improve our methodology while maintaining clean out of sample validation. This approach becomes increasingly important as we plan to further expand and adapt our already extensive data coverage next year, creating new opportunities for systematic research. For institutional data buyers, the key question is not simply how much live history a new data product has, but whether its historical performance is supported by a consistent and independently validated framework.A new data product cannot offer years of live history from day one. But it can offer a rigorous framework for evaluating the credibility of its historical results. #AlternativeData #QuantResearch #DataQuality #Backtesting #HedgeFunds

Needlestacks Nervous Assets Are Now Available on the Eagle Alpha Data Buyer Platform We are pleased to announce that our Nervous Asset data products are now available on the Eagle Alpha Data Buyer Platform. Our proprietary Nervous Asset Framework identifies statistically leading instruments and persistent cross-asset relationships to support predictive market analytics across multiple investment horizons. Four product families, now accessible through Eagle Alpha: Asset Relationship Mapping API Identifies statistically leading cross-asset relationships, quantifies their forecasting strength, and captures target reaction times. Model Features API Delivers engineered, model-ready feature vectors for quantitative strategies and machine learning pipelines. Probabilistic Scenario Forecast API Translates cross-asset dynamics into scenario probabilities across different price-movement thresholds and investment horizons. Trading Signals API Converts probability-weighted forecasts into actionable buy, sell, or hold signals, including confidence scores and validity parameters. Each product is available in two market coverage configurations: US-only: Intraday coverage focused on commodities and US equities. Non-US: Broad international asset coverage, with a strong focus on Asian markets. From cross-asset relationships and engineered features to probabilistic forecasts and systematic trading signals, our products enable quantitative teams to integrate predictive intelligence into their existing workflows. A special thank you to Brendan Furlong for his support throughout the onboarding process and for helping bring our products to the Eagle Alpha Data Buyer Platform.Explore our Nervous Asset products directly on the Eagle Alpha Data Buyer Platform. #DataBuyer #AlternativeData #QuantitativeFinance #CrossAssetIntelligence

Excited that Needlestack Technologies Ltd. is sponsoring ICAIF - ACM International Conference on AI in Finance 2026 – the 7th ACM International Conference on AI in Finance, taking place in Milan, Italy! 🇮🇹 The event kicks off this weekend with an impressive lineup of workshops led by renowned researchers and organizers, covering exciting topics such as Reinforcement Learning, Large Language Models, and their applications in finance, followed by the main conference. I am looking forward to being there in person, supporting the event, exchanging ideas with researchers and industry leaders, and exploring the latest developments shaping the future of AI in global finance. See you in Milan! 🚀 #ICAIF2026 #AIinFinance #ReinforcementLearning #LLMs #FinTech

🚀 Our new paper is out in Scientific Reports! Last week, we published our paper “Fair distributed machine learning with imbalanced data as a Stackelberg evolutionary game”. We explore how game theory can help make federated learning fairer, especially when data is unevenly distributed across participants. By introducing two Stackelberg-based weighting approaches, we show that underrepresented participants can benefit significantly without substantially compromising the performance of larger participants. A big thank you to my co-authors Ingo Roeder and nico scherf for the great collaboration!

Most alternative data research starts with a story. Low river levels hurt chemical producers. Ship traffic predicts commodity exports. The story is intuitive, which is exactly why it is crowded. Some of the most interesting relationships do not follow a clear narrative at all. The variance of ship arrival times, not the volume of ships, can say more about implied volatility in freight sensitive stocks than about their direction. Dispersion across regional electricity loads can predict dispersion within industrial stocks, even when it says nothing about the index. When two datasets that usually move together start to decouple, such as hiring data and web traffic, the break itself can mark a regime change. Missing data is information. When ships switch off their transponders or companies suddenly stop posting jobs, the gap can be the signal. Many datasets only matter beyond a threshold. River levels are irrelevant for months and then dominate margins within weeks. Linear models miss this entirely. Companies that share suppliers in procurement records often move together after shocks, even without any obvious economic link between them. This is why looking across many assets matters. A dataset may predict nothing about returns and still predict volatility, dispersion, correlation or tail risk somewhere else. The tradeoff is real. Without a narrative, overfitting becomes the main risk. These relationships need economic plausibility checks, strict out of sample testing and stability across regimes. The edge is often not in the story everyone can tell. It is in the structure nobody bothered to measure. #AlternativeData #CrossAsset #QuantitativeResearch #SystematicTrading