Collections
Curated reading paths through the blog, ordered from basics to advanced.
Backtesting Without Fooling Yourself
A step-by-step path from what your backtest really optimizes to proving an edge survives overfitting, multiple testing, and live execution. Read top to bottom — each part builds on the last.
- 01Maqsad funksiyasini loyihalash: siz optimallashtirgan metrika strategiyangizni yashirincha tanlaydi
- 02Ko'p vaqt oralig'idagi bektestlarda kelajakka qarash yo'qligini isbotlash: kelajakni buzib, o'tmish uni ko'ra olmasligini isbotlash
- 03Look-Ahead Bias: bitta bar xatosi sof shovqindan qanday qilib 15 ta Sharpe yasaydi
- 04Walk-Forward Optimization: The Only Honest Strategy Test
- +7 more
High-Performance Backtest Engines
How to build a backtest engine that runs hundreds of times faster without changing a single PnL number — data layout, caching, adaptive resolution, and architecture, from first speedups to production internals.
- 01Backtest tezligi zinapoyasi: laptop CPU'sida 298x, oxirgi bitimigacha bir xil PnL
- 02Freymvork solig'i: qachonki backtest kutubxonangiz oddiy pandas siklidan sekinroq bo'ladi
- 03Yig'ilgan Parquet Kesh: Ko'p Vaqt Oralig'idagi Backtestlarni Yuzlab Marta Tezlashtirish
- 04Ikki oʻqli parametr fazosi: nega sweepingizning katta qismi deyarli bepul boʻlishi kerak
- +7 more
Complex Arbitrage in Rust
A six-part build-up of multi-leg crypto arbitrage — from negative-cycle detection to the linear algebra, copulas, and machine learning behind it, ending in low-latency Rust execution.
- 01Arbitrajni aniqlash uchun graf algoritmlari: Bellman-Forddan RICH gacha
- 02Fyuchers-Spot Arbitraji: Cash-and-Carry-dan DeFi-CeFi-gacha
- 03Matritsalar, tenzorlar va tropik algebra: arbitrajni aniqlash uchun chiziqli algebra
- 04Arbitraj uchun Vine Copulas: yuqori o'lchamli bog'liqliklarni modellashtirish
- +2 more
Order Book & Market Microstructure
How the order book really works — accessing the data, reading queue position, rebuilding bars from order flow, and modeling it with deep learning and Hawkes processes.
- 01CCXT: WebSocket order book metodlari aslida qanday ishlaydi
- 02Algoritmik Treydingda Order Turlari: Chasing bilan Limitdan Virtual Orderlargacha
- 03Queue Inside the Wall: Analyzing Order Position in Order Book Density
- 04Algoritmik treyding uchun bar turlari va agregatsiya usullari
- +8 more
Portfolio Construction & Risk
From Markowitz to production HRP + CVaR: how to allocate across crypto assets, model tail dependence with copulas, and size positions without blowing up.
- 01Kripto uchun Markowitz portfel nazariyasi: noldan qahramongacha
- 0212 Portfolio Optimization Algorithms, Compared: HRP, Black-Litterman, NCO and Beyond
- 03Inside Our House Algorithm: HRP + Long/Short + CVaR with Hull-White
- 04Kripto portfellarida birgalikdagi xavfni modellashtirish uchun kopula modellari
- +1 more
Risk & Position Sizing
Build position sizes from drawdown math, realistic trading costs, and uncertainty in backtest results before applying Kelly.
- 01Zarar-Foyda Asimmetriyasi: Depozitingizni Yo'q Qiluvchi Matematika
- 02Slippage konstantalari emas, slippage egri chiziqlari: jonli savdo bilan aloqaga kirganda omon qoladigan xarajat modellari
- 03Monte-Karlo Bootstrap: bor-yo'g'i 10 qator kod bilan backtest uchun ishonch intervallarini qanday olish mumkin
- 04Strategiyalar uchun Kelli mezoni: pozitsiya hajmini qanday belgilash va kapitalni taqsimlash kerak
Statistical Arbitrage & Pairs Trading
Trade the spread between correlated assets — from the distance approach to cointegration and Kalman filters, then dynamically combining mean reversion with momentum.
- 01Juftlik savdosida masofa yondashuvi: Rust yordamida amalga oshirish va tahlil
- 02Statistical Arbitrage and Pairs Trading in Crypto Markets: From Cointegration to the Kalman Filter
- 03Signal Correlation: How Many Pairs to Monitor
- 04Statistik Arbitrajda O'rtaga Qaytish va Momentum Strategiyalarini Dinamik Birlashtirish: Matematik Asoslar va Amaliy Qo'llanma
- +1 more
Deep Learning for Markets
Neural forecasting for crypto — transformers, diffusion models, and foundation models, and how conformal prediction keeps their uncertainty honest.
- 01Temporal Fusion Transformers for Multi-Horizon Portfolio Forecasting
- 02Diffuzion Modellar va Kriptovalyuta Tartibsizligi: Nega DDPM Bitcoin Qulashini Munajjimdan Ko'ra Yaxshiroq Bashorat Qila Oladi
- 03Kronos: sham (candlestick) grafiklarini transformer tilida gapirishga o'rgatuvchi asos model
- 04Xavf-xatarni Hisobga Oluvchi Pozitsiya Hajmini Belgilash uchun Konformal Bashorat
AI Agents for Trading
The agentic-AI stack for markets — multi-agent frameworks, open-source hedge funds, and LLMs that mine alpha from earnings calls.
- 01Agentic AI yordamida investitsiya portfelini boshqarishdagi inqilob
- 02TradingAgents: Multi-Agent AI Framework That Models a Hedge Fund
- 03AI4Finance Foundation: Algo-treyding uchun FinGPT, FinRL va FinRobot ekotizimi
- 04AI Hedge Fund: sun'iy intellekt tahlilchilari savdolar bo'yicha ovoz beradigan ko'p agentli fond
- +1 more
QuestDB for Algorithmic Trading
Stand up a time-series stack for trading on QuestDB — from architecture to the SQL that matters, to a production deployment.
- 01QuestDB for Algorithmic Trading: Architecture That Speaks the Language of Markets
- 02QuestDB for Algorithmic Trading: SQL Extensions That Change the Game
- 03QuestDB for Algorithmic Trading: From Order Books to Production Architecture
Low-Latency Trading Infrastructure
The plumbing under an HFT stack — how components talk (WebSocket, FIX, gRPC, Aeron), messaging on Aeron and Zig, and a C++ FIX/FAST scalper.
- 01Algo Treyding Tizimlarida Ma'lumotlar Aloqasi: Texnologik Sharh
- 02Aeron: HFT sohasining yarmini harakatga keltiruvchi xabar almashish tizimining ichki tuzilishi
- 03ZigBolt: Why We Built Our Own Aeron in Zig and Hit 20 Nanoseconds Per Message
- 04Developing a Simple C++ Scalper Using FAST/FIX: Step-by-Step Guide
Execution & Market Making
From TWAP/VWAP and Almgren-Chriss to TCA, slippage models, and the Avellaneda-Stoikov market maker — how to turn a signal into fills without paying the spread twice.
- 01TWAP vs VWAP vs POV: bajarilish benchmarkini tanlash (va har biri qachon sizni aldashini bilish)
- 02Almgren-Chriss usulini chala tushuntirishlarsiz: bir kunda amalga oshirish mumkin bo'lgan optimal ijro
- 03Slice ichida: sizning rejalashtiruvchingiz va birja o'rtasidagi child-order taktikasi
- 04Kriptovalyutada Smart Order Routing: Bitta Order, O'n Ikkita Venue, NBBO Yo'q
- +7 more
Onchain & DeFi Quant
Quantitative DeFi from first principles — MEV and sandwich attacks, atomic arbitrage, liquidation cascades on lending protocols, and LP profitability on Uniswap v3.
- 01MEV anatomiyasi: sendvichlar, frontrunning va mempoolning qorong'u o'rmoni
- 02The MEV supply chain: PBS, MEV-Boost, and who actually captures the value
- 03On-chain arbitraj: atomik tsikllar, flesh kreditlar va yutishingiz kerak bo'lgan auksion
- 04On-chain likvidatsiyalar: Aave va Compound mexanikasi va ular atrofidagi bot biznesi
- +5 more
Causality & Lead-Lag in Markets
Who moves whom — Granger causality, transfer entropy, PCMCI discovery, and the causal ML toolbox (double ML, causal forests, synthetic control) applied to crypto markets.
- 01Toda-Yamamoto vs Differenced Granger: Does the BTC Lead-Lag Survive?
- 02Transfer Entropy: Which Way Does Information Flow Between Crypto Assets?
- 03PCMCI: Ko'p o'zgaruvchan kriptovalyuta seriyasida sabablarni aniqlash
- 04Savdo-sotiqda Heterogen Davolash Ta'sirlari uchun Kausal O'rmonlar
- +3 more
Volatility Modeling & Regimes
Forecast volatility with the GARCH family — asymmetry and leverage effects, dynamic correlations, vol targeting — then detect market regimes with HMMs and trade adaptively.
- 01GARCH(1,1): Kripto o'zgaruvchanligini bashorat qilish
- 02Assimetrik va Og'ir Dumli GARCH: EGARCH, GJR va Student-t
- 03DCC-GARCH: Pair savdo va portfel riski uchun dinamik korrelatsiyalar
- 04Volatillikni maqsad qilish va GARCH prognozlari bilan savdo qilish
- +1 more
Modern ML for Trading
The full gradient-boosting-to-neural-operator toolkit — ensembles, AutoML, probabilistic forecasting, distillation and pruning for latency, plus physics-informed architectures like Neural ODEs and Fourier operators.
- 01XGBoost for Return Direction: Class Imbalance and Decision Thresholds
- 02Ansambl usullari: mustahkam alfa uchun zaif o'quvchilarni birlashtirish
- 03Tizimli savdo quvurlari uchun AutoML
- 04Bir vaqtning o'zida narx, hajm va o'zgaruvchanlikni bashorat qilish uchun ko'p vazifani o'rganish
- +11 more