[{"data":1,"prerenderedAt":97},["ShallowReactive",2],{"work-stock-market-deep-learning":3,"work-next-\u002Fwork\u002Fstock-market-deep-learning":94},{"id":4,"title":5,"accent":6,"body":7,"client":59,"cover":60,"coverAlt":60,"description":61,"duration":62,"extension":63,"featured":64,"industry":65,"meta":66,"navigation":64,"order":54,"path":67,"quote":68,"results":72,"seo":79,"services":80,"stack":83,"stem":91,"year":92,"__hash__":93},"work\u002Fwork\u002Fstock-market-deep-learning.md","Deep-learning market analysis for a fintech","#1A4380",{"type":8,"value":9,"toc":52},"minimark",[10,15,19,23,39,43,46],[11,12,14],"h2",{"id":13},"challenge","Challenge",[16,17,18],"p",{},"The client's analysts were building signals by hand in spreadsheets, with no reproducible backtesting and no way to combine price data with news sentiment.",[11,20,22],{"id":21},"what-we-built","What we built",[24,25,26,30,33,36],"ul",{},[27,28,29],"li",{},"A data pipeline pulling price, volume and news feeds into a versioned feature store.",[27,31,32],{},"Sequence models (LSTM\u002Ftemporal CNN) for short-horizon trend classification, combined with a transformer-based sentiment scorer over news headlines.",[27,34,35],{},"Walk-forward validation with transaction costs, benchmarked against a simple momentum baseline.",[27,37,38],{},"Dashboards showing signal confidence, exposure and drift, and a written model card describing limits and failure modes.",[11,40,42],{"id":41},"results","Results",[16,44,45],{},"In walk-forward testing the model-guided allocation improved returns by about 22% relative to the client's baseline strategy while reducing drawdown. More importantly, the team now has a repeatable research process instead of ad-hoc spreadsheets.",[47,48,49],"blockquote",{},[16,50,51],{},"Financial models are probabilistic tools, not oracles. Every report ships with its confidence intervals.",{"title":53,"searchDepth":54,"depth":54,"links":55},"",2,[56,57,58],{"id":13,"depth":54,"text":14},{"id":21,"depth":54,"text":22},{"id":41,"depth":54,"text":42},"Fintech · investment platform (Middle East)",null,"Time-series and sentiment models feeding a portfolio-monitoring platform for an investment-focused fintech.","14 weeks","md",true,"Finance & Fintech",{},"\u002Fwork\u002Fstock-market-deep-learning",{"text":69,"name":70,"role":71},"What we valued most was the honesty: a clear baseline, a model card, and a number we could take to the board.","COO","Client (attribution on request)",[73,76],{"value":74,"label":75},"22%","improvement in model-guided returns vs. baseline strategy",{"value":77,"label":78},"Daily","automated retraining and reporting",{"title":5,"description":61},[81,82],"data-analytics-predictive-ai","nlp",[84,85,86,87,88,89,90],"Python","PyTorch","TA-Lib","Pandas","Airflow","Plotly","AWS","work\u002Fstock-market-deep-learning","2023","jEbxtz3LjE_SkERnpFq_LtpOy8mofpSCDcOg17Lu8fU",{"path":95,"title":96},"\u002Fwork\u002Fhealthcare-data-analysis","Clinical data analysis for a hospital network",1792973679704]