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Crypto Market Intelligence & Alpha Signal Engine
An end-to-end Colab pipeline ingests market feeds, engineers 43 signals, detects regimes (KMeans/HMM/GMM), models 24‑hour alpha with a Random Forest (ROC‑AUC 0.7714), flags anomalies (Isolation Forest/Autoencoders), and backtests strategies; key findings: the SELL signal is highly precise (only 5.14% of SELLs rose next day), anomalies are often bullish (36.87% up vs 25.49% normal), price‑level context and regime probabilities drive predictions, and the model favors low‑volatility, defensive assets during downturns.
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The Financial Performance Analysis Dashboard provides a comprehensive overview of an organization’s financial health by analyzing revenue, expenses, profitability, and key financial ratios. Developed using Power BI, this dashboard enables finance teams and decision-makers to monitor business performance, track profitability trends, and evaluate financial efficiency in real time.
Key KPIs Tracked
✔ Revenue – Total income generated from business operations
✔ Expenses – Combined operational and non-operational costs
✔ Gross Profit – Revenue remaining after cost of goods sold (COGS)
✔ EBIT – Earnings before interest and taxes, indicating operational profitability
✔ Net Profit – Final profit after all expenses, interest, and taxes
dashboarddashboarddesignpowerbidashboardMicrosoft Power BIData ScienceData VisualizationAI Engineer Microsoft ExcelTableau
Amazing work
looks cool to me 🔥
Built a Multimodal RAG system live on stream. From zero to working CLI in one session.
The goal: a terminal-based RAG pipeline using Haystack, Gemini Multimodal Embeddings, and Flash Lite, querying complex research PDFs through a conversational loop.
Three real engineering lessons from the build:
Chunking isn't optional. Raw PDFs exhaust token limits instantly. 6-page splits solved it.
InMemoryDocumentStore works for prototyping, but re-vectorizes on every launch. Persistent DBs like Weaviate are the next step.
Haystack's pipeline is strict. Mis-wiring a retriever to a generator crashes the loop immediately. API contracts matter.
Result: a CLI that maps user queries to the top 4 semantic chunks in RAM and returns grounded, non-hallucinated answers from your own documents.
Every mistake, every fix, on camera.
"Ugurcan Uzunkaya this is seriously impressive! 🔥 Building a full RAG pipeline live on stream in one session is no joke!
That chunking lesson is so real — token limits hit different when you're working with raw PDFs.
And the Haystack pipeline point about API contracts is...
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