# thequant.space > A scored, machine-readable archive of 5399 quantitative-finance research papers from arXiv q-fin and SSRN. Every paper has a plain-language summary, a research flowchart (Mermaid), a math-complexity score and an empirical-rigor score (0-10 each) with a written rationale, a quadrant label, topic-hub assignments, method and asset-class labels, extracted keywords, and a code link when the paper provides one. Plus 68 practitioner guides on evaluating, reproducing and operationalizing quant research, 4 browser calculators, and monthly/weekly reports derived from the corpus. Updated daily. Terms and programmatic access are documented at https://thequant.space/agents/ . Data is free for personal and research use with attribution to thequant.space. ## How papers are scored - Math complexity (0-10): theoretical overhead — 7+ means stochastic calculus, PDEs, bespoke proofs; 1-3 means descriptive statistics or conceptual frameworks. - Empirical rigor (0-10): path to implementation — 7+ means high-fidelity data, transaction costs, out-of-sample validation; 1-3 means toy or synthetic data or no data. - Quadrants: Holy Grail (high math, high rigor), Street Traders (low math, high rigor), Lab Rats (high math, low rigor), Philosophers (low math, low rigor). - hub_score = 0.6 * rigor + 0.4 * math is the default ranking across the site. - Scores are LLM-assisted judgements against a fixed rubric, not peer review. Full methodology: https://thequant.space/score-guide/ ## Machine-readable data - [All scored papers, JSON](https://thequant.space/data/papers.json): one record per paper with every field above (about 4 MB, gzip-compressed in transit). Keys: paper_id, title, paper_date, authors, topics, asset_classes, methods, paper_type, primary_category, quadrant, math_complexity, empirical_rigor, hub_score, code_url, doi, journal_ref, paper_url, page_url, tags. - Per-topic shards, same record shape, 70-1000 KB each: https://thequant.space/data/topics/.json (slugs listed under Topics below). - [Scores for the quadrant chart, JSON](https://thequant.space/data/all_papers.json): title, url, math, rigor only. - Every paper page embeds a schema.org ScholarlyArticle JSON-LD block carrying the same scores, and a BibTeX entry. - [RSS feed](https://thequant.space/index.xml): newest 50 pages, full text. - [Sitemap](https://thequant.space/sitemap.xml) - [Agent guide](https://thequant.space/agents/): usage terms, attribution string, field semantics, how to cite. - Full text of all guides and methodology pages in one file: https://thequant.space/llms-full.txt ## Topics - [Commodities & Energy Markets](https://thequant.space/topics/commodities-energy/): Research on crude oil, natural gas, electricity and power markets, carbon pricing, metals, and agricultural commodities — pricing, forecasting, hedging, and trading. (203 papers; data: https://thequant.space/data/topics/commodities-energy.json) - [Crypto Markets & DeFi](https://thequant.space/topics/crypto-defi/): Research on cryptocurrency markets, DeFi protocols, AMMs, stablecoins, and blockchain market structure. (603 papers; data: https://thequant.space/data/topics/crypto-defi.json) - [Factor Investing & Asset Pricing](https://thequant.space/topics/factor-investing/): Research on factor models, return anomalies, momentum, the cross-section of returns, and empirical asset pricing. (363 papers; data: https://thequant.space/data/topics/factor-investing.json) - [Fixed Income & Interest Rates](https://thequant.space/topics/fixed-income/): Research on yield curves, term structure models, bond markets, and credit spreads. (351 papers; data: https://thequant.space/data/topics/fixed-income.json) - [High-Frequency Trading & Optimal Execution](https://thequant.space/topics/high-frequency-trading/): Research on HFT, optimal execution, TWAP/VWAP algorithms, and intraday trading strategies. (337 papers; data: https://thequant.space/data/topics/high-frequency-trading.json) - [Insurance & Actuarial Risk](https://thequant.space/topics/insurance-actuarial/): Research on insurance pricing, reinsurance, annuities and pensions, mortality and longevity risk, solvency, and risk sharing — scored for math complexity and empirical rigor. (302 papers; data: https://thequant.space/data/topics/insurance-actuarial.json) - [Machine Learning for Market Forecasting](https://thequant.space/topics/machine-learning/): Research using deep learning, LSTMs, transformers, and tree ensembles to forecast returns, prices, and market states. (1253 papers; data: https://thequant.space/data/topics/machine-learning.json) - [Market Microstructure](https://thequant.space/topics/market-microstructure/): Research on limit order books, market making, price impact, and liquidity — scored for math complexity and empirical rigor. (579 papers; data: https://thequant.space/data/topics/market-microstructure.json) - [Market Simulation & Agent-Based Models](https://thequant.space/topics/market-simulation/): Research on agent-based market simulators, synthetic order books and price series, generative market models, LLM trading agents, and backtesting engines. (226 papers; data: https://thequant.space/data/topics/market-simulation.json) - [NLP & LLMs in Finance](https://thequant.space/topics/nlp-llm/): Research on sentiment analysis, news analytics, and large language models applied to trading and financial analysis. (509 papers; data: https://thequant.space/data/topics/nlp-llm.json) - [Options & Derivatives Pricing](https://thequant.space/topics/options-derivatives/): Research on option pricing, hedging, implied volatility, and derivatives markets — from Black-Scholes extensions to deep hedging. (595 papers; data: https://thequant.space/data/topics/options-derivatives.json) - [Portfolio Optimization](https://thequant.space/topics/portfolio-optimization/): Research on portfolio construction, mean-variance optimization, risk parity, and allocation under uncertainty — scored for rigor and complexity. (633 papers; data: https://thequant.space/data/topics/portfolio-optimization.json) - [Reinforcement Learning for Trading](https://thequant.space/topics/reinforcement-learning/): Research applying reinforcement learning — DQN, policy gradients, actor-critic — to trading, execution, and portfolio problems. (319 papers; data: https://thequant.space/data/topics/reinforcement-learning.json) - [Risk Management & Tail Risk](https://thequant.space/topics/risk-management/): Research on VaR, expected shortfall, tail risk, systemic risk, and stress testing — scored for empirical rigor. (823 papers; data: https://thequant.space/data/topics/risk-management.json) - [Statistical Arbitrage & Pairs Trading](https://thequant.space/topics/statistical-arbitrage/): Research on statistical arbitrage, pairs trading, cointegration, and mean-reversion strategies — scored for math complexity and empirical rigor. (89 papers; data: https://thequant.space/data/topics/statistical-arbitrage.json) - [Stochastic Control & Optimal Stopping](https://thequant.space/topics/stochastic-control/): Research on stochastic control, optimal stopping, HJB equations, mean-field games, and BSDEs applied to investment, consumption, liquidation, and dividend problems. (512 papers; data: https://thequant.space/data/topics/stochastic-control.json) - [Volatility Modeling & Forecasting](https://thequant.space/topics/volatility/): Research on GARCH, realized volatility, rough volatility, the VIX, and volatility forecasting across asset classes. (746 papers; data: https://thequant.space/data/topics/volatility.json) ## Guides Practical guides for evaluating, reproducing, and operationalizing quant research. Decision frameworks, not textbook material. - [A Checklist for Reproducing Quant Research](https://thequant.space/guides/reproduce-quant-research/): A six-stage checklist for reproducing quantitative finance papers: acquisition, alignment, independent reimplementation, reconciliation, stress testing, and documentation — with the failure modes at each stage. - [A Minimal ML Experiment-Tracking Stack for Quant Research](https://thequant.space/guides/ml-experiment-tracking/): Experiment tracking for quant ML: what to record, the finance-specific requirements (trial counts, temporal splits, leakage audits), tool tiers from SQLite to MLflow/W&B, and the minimal stack that suffices. - [A Practical Quant Research Stack for a One-Person Shop (2026)](https://thequant.space/guides/quant-research-stack/): The complete toolchain for solo quant research in 2026: data, storage, backtesting, compute, and deployment — with honest costs and the mistakes to skip. - [A Visual Map of Alternative-Data Research](https://thequant.space/guides/map-alternative-data/): A Visual Map of Alternative-Data Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored. - [A Visual Map of Limit-Order-Book Prediction Research](https://thequant.space/guides/map-lob-prediction/): A Visual Map of Limit-Order-Book Prediction Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored. - [A Visual Map of Machine Learning in Asset Pricing](https://thequant.space/guides/map-ml-asset-pricing/): A Visual Map of Machine Learning in Asset Pricing: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored. - [A Visual Map of Market-Making Research](https://thequant.space/guides/map-market-making/): A Visual Map of Market-Making Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored. - [A Visual Map of Reinforcement Learning for Trading](https://thequant.space/guides/map-rl-trading/): A Visual Map of Reinforcement Learning for Trading: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored. - [A Visual Map of Statistical-Arbitrage Research](https://thequant.space/guides/map-statistical-arbitrage/): A Visual Map of Statistical-Arbitrage Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored. - [A Visual Map of Volatility Forecasting Research](https://thequant.space/guides/map-volatility-forecasting/): A Visual Map of Volatility Forecasting Research: the field organized into its major branches, with the strongest papers in each ranked by empirical rigor. Auto-refreshed as new papers are scored. - [Alpha, Beta, and Alternative Risk Premia: The Difference That Prices Everything](https://thequant.space/guides/alpha-beta-alternative-risk-premia/): The alpha/beta/ARP taxonomy: what each actually is, the regression that sorts any return stream, why the boundaries move over time, and what each category should cost. - [Auditing a Monte Carlo: Method Notes from a Leveraged-ETF Simulation](https://thequant.space/guides/auditing-a-monte-carlo/): Every technique used to take four Monte Carlo simulation scripts apart, find what they were actually measuring, and rebuild them — with the real numbers each step produced. - [Capacity Constraints in Quantitative Strategies](https://thequant.space/guides/capacity-constraints/): How to estimate a strategy's capacity: the impact arithmetic, the capacity hierarchy by strategy type, self-competition effects, and why capacity is the number papers never report. - [Combinatorially Symmetric Cross-Validation (CSCV) Explained](https://thequant.space/guides/cscv-explained/): CSCV and the Probability of Backtest Overfitting (PBO) explained: the block-combination construction, pseudocode, how to read PBO, and the method's honest limitations. - [Corporate Actions and Adjusted Price Data](https://thequant.space/guides/corporate-actions-adjusted-prices/): Corporate actions in quant research: how adjustments work, the dividend/total-return distinction, the actions that break backtests (spinoffs, mergers, delistings), and the store-unadjusted principle. - [Cross-Sectional Momentum vs Time-Series Momentum](https://thequant.space/guides/cross-sectional-vs-time-series-momentum/): The two momentum families compared: construction, crash profiles, evidence bases, and why they are different strategies that happen to share a name. - [Cross-Sectional vs Time-Series Predictability: Two Different Claims](https://thequant.space/guides/cross-sectional-vs-time-series-predictability/): The difference between cross-sectional and time-series predictability in finance: what each claim means, how evidence standards differ, and why conflating them produces phantom strategies. - [Event-Driven Trading Research: Event Definitions, Leakage, and Execution](https://thequant.space/guides/event-driven-research/): The methodology of event studies for trading: defining events without hindsight, the announcement-timestamp problem, abnormal-return construction, and the execution window where paper edges vanish. - [Free vs Paid Market Data: What Changes in Real Research](https://thequant.space/guides/free-vs-paid-market-data/): What separates free market data from paid: the six quality dimensions that matter for research, where free data is genuinely sufficient, and the failure modes that only surface after you've built on it. - [From Notebook to Production: A Minimal Automated-Strategy Operating Stack (2026)](https://thequant.space/guides/notebook-to-production/): The minimal operating stack for running an automated trading strategy: one VPS, Docker Compose, Postgres, cron done right, secrets, logs, dead-man alerting, backups, and incident response — with working configs. - [How to Backtest Prediction-Market Strategies Without Fooling Yourself (2026)](https://thequant.space/guides/prediction-market-backtesting/): Prediction-market backtests fail differently than equity backtests: resolution leakage, phantom liquidity, and survivorship in resolved markets. A methodology that survives contact with live books. - [How to Build a Quant Research Pipeline: From Idea to Evidence, Repeatably](https://thequant.space/guides/quant-research-pipeline/): The seven-stage quant research pipeline — idea intake, data, features, backtest, validation, paper trading, production — with the artifact each stage must produce and the gate it must pass. - [How to Choose Which Papers to Replicate First](https://thequant.space/guides/choose-papers-to-replicate/): A prioritization framework for replication: the expected-information calculation, the five selection criteria, portfolio-of-replications thinking, and which paper types repay the effort. - [How to Distinguish an Academic Contribution from an Implementable Idea](https://thequant.space/guides/academic-vs-implementable/): A framework for separating papers that advance knowledge from papers you can trade: the five implementability tests, why both kinds have value, and how the quadrant system encodes the distinction. - [How to Evaluate a Factor-Investing Paper](https://thequant.space/guides/evaluate-factor-investing-paper/): A checklist for evaluating factor-investing and cross-sectional asset-pricing papers: multiple-testing hurdles, portfolio construction choices, cost realism, and the questions that expose a factor-zoo entry. - [How to Evaluate a Machine-Learning Trading Paper](https://thequant.space/guides/evaluate-ml-trading-paper/): A checklist for evaluating ML trading papers: baseline honesty, leakage-prone pipelines, accuracy-vs-P&L confusion, seed variance, and the questions that separate signal from citation bait. - [How to Evaluate a Market-Microstructure Paper](https://thequant.space/guides/evaluate-microstructure-paper/): A checklist for evaluating market-microstructure papers: dataset provenance, venue and period specificity, theoretical assumption audits, and the generalization trap. - [How to Evaluate a Reinforcement-Learning Trading Paper](https://thequant.space/guides/evaluate-rl-trading-paper/): A checklist for evaluating RL trading papers: environment leakage, reward hacking, the sample-efficiency problem in non-stationary markets, and where RL claims are actually credible. - [How to Find Code for Finance Research Papers](https://thequant.space/guides/find-code-finance-papers/): Locating implementations of quant finance papers: official repos, third-party reimplementations and their risks, the library ecosystem, and how to audit found code before trusting it. - [How to Find the Datasets Used in Quant-Finance Papers](https://thequant.space/guides/find-datasets-quant-papers/): Locating the data behind quant finance papers: the standard dataset zoo (CRSP, TAQ, FI-2010, Kaggle mirrors), decoding data sections, access tiers, and legitimate substitutes when the original is unreachable. - [How to Interpret a Factor-Zoo Paper](https://thequant.space/guides/interpret-factor-zoo-paper/): Reading factor-zoo and replication meta-studies: what the multiple-testing corrections mean, why replication rates differ so wildly between studies, and what survives for practitioners. - [How to Interpret Confidence Intervals in Strategy Research](https://thequant.space/guides/confidence-intervals-strategy-research/): Reading confidence intervals on Sharpe ratios, alphas, and backtest statistics: what they actually say, how wide they really are, block bootstrapping, and the selection problem that breaks naive intervals. - [How to Read a Quant Finance Paper (Without Wasting Your Afternoon)](https://thequant.space/guides/how-to-read-quant-finance-papers/): A triage framework for reading quantitative finance papers: the 10-minute pass, the two axes that matter, section-by-section priorities, and the red flags that end a read early. - [How to Search Quantitative-Finance Literature Efficiently](https://thequant.space/guides/search-quant-finance-literature/): A working system for searching quant finance literature: where each source excels (arXiv, SSRN, Google Scholar), citation-chain tactics, freshness triage, and search operators that actually work. - [How to Store Tick Data Efficiently](https://thequant.space/guides/store-tick-data-efficiently/): Practical tick-data storage: partitioning schemes, columnar formats, compression choices, type discipline, and the layout decisions that make years of ticks queryable on one machine. - [How to Tell Whether a Trading Backtest Is Real](https://thequant.space/guides/evaluate-trading-backtest/): A practical framework for evaluating trading backtests: the eight questions that expose fake performance, the metrics that must be present, and the verdict rules we apply to published research. - [How to Version Datasets and Backtests](https://thequant.space/guides/versioning-datasets-backtests/): Versioning for quant research: content-addressed datasets, config-hashed backtests, the lineage chain that makes any result re-derivable, and the lightweight tooling that suffices. - [Limit-Order-Book Imbalance: What It Measures and What It Misses](https://thequant.space/guides/limit-order-book-imbalance/): Order-book imbalance as a predictor: why it works at tick horizons, the spoofing and iceberg problems, the monetization gap, and how to evaluate imbalance-based research. - [Local GPU vs Cloud GPU for Financial NLP and LLM Research (2026)](https://thequant.space/guides/local-vs-cloud-gpu/): When to buy a GPU and when to rent one for financial NLP and LLM research: break-even math, VRAM sizing, data-licensing constraints, and the hybrid default. - [Look-Ahead Bias and Point-in-Time Data: The Complete Taxonomy](https://thequant.space/guides/look-ahead-bias-point-in-time-data/): Every way future information leaks into backtests: restated fundamentals, index membership, same-bar execution, timestamp semantics, and LLM training-data leakage — with detection tests for each. - [Market Data for Quant Research: How to Choose a Vendor (2026)](https://thequant.space/guides/market-data-vendors/): A decision framework for choosing market data vendors for quant research: survivorship bias, point-in-time integrity, licensing, and the real cost tiers. - [Market Making: Inventory Risk, Adverse Selection, and Spread Capture](https://thequant.space/guides/market-making-mechanics/): The three forces of market making — spread capture, inventory risk, and adverse selection — with the Avellaneda-Stoikov intuition, the profitability identity, and the production failure catalog. - [Microstructure Noise and Realized Volatility](https://thequant.space/guides/microstructure-noise-realized-volatility/): Why high-frequency volatility estimates explode: bid-ask bounce, discreteness, the signature plot, noise-robust estimators, and the practical sampling rules for realized volatility. - [OHLCV, Trade, Quote, and Order-Book Data: What Each Can Answer](https://thequant.space/guides/market-data-types/): The market-data hierarchy from daily bars to full order books: what each granularity can and cannot answer, the storage and cost jumps between levels, and matching data type to research question. - [Options-Implied Volatility: A Practical Research Primer](https://thequant.space/guides/options-implied-volatility-primer/): Implied volatility for researchers: what IV actually is, surface anatomy, the variance risk premium, using IV as a signal, and the data pitfalls that corrupt options research. - [P-Hacking in Financial Research: The Practices, the Tells, the Fixes](https://thequant.space/guides/p-hacking-financial-research/): How p-hacking works in finance: the seven questionable research practices, the tells visible in published papers, and the reader-side corrections that keep you from funding other people's noise. - [Pairs Trading: Assumptions, Cointegration, and Failure Modes](https://thequant.space/guides/pairs-trading-failure-modes/): The assumptions under pairs trading and how each fails: cointegration's fragility, the selection problem, divergence risk, and the post-2000s decay of the classic approach. - [Point-in-Time Data Architecture for Backtesting](https://thequant.space/guides/point-in-time-data-architecture/): Designing a point-in-time data layer: bitemporal modeling, the as-of query pattern, a practical schema for prices, fundamentals, universes and events, and the migration path from a naive store. - [Portfolio Optimization: Estimation Error and Regularization](https://thequant.space/guides/portfolio-optimization-estimation-error/): Why mean-variance optimization amplifies estimation error, the error-maximization mechanism, the regularization toolkit (shrinkage, constraints, resampling), and the 1/N benchmark that keeps everyone honest. - [Regime Dependence: When a Historical Edge Stops Working](https://thequant.space/guides/regime-dependence/): Regime dependence in trading strategies: why edges are conditional on market states, how to detect regime-carried backtests, decay vs regime-shift diagnosis, and what to do when an edge goes quiet. - [Reproducible Quant Research with Docker](https://thequant.space/guides/docker-reproducible-research/): Using Docker for reproducible quant research: what containers do and don't solve, the research-image pattern, determinism beyond the environment, and when containers are overkill. - [Research-to-Production Checklist for an Automated Trading Strategy (2026)](https://thequant.space/guides/production-checklist/): The complete checklist for taking a backtested strategy live: validation gates, execution safety, kill switches, monitoring, reconciliation, and the go-live protocol. - [Risk Parity, Minimum Variance, and Maximum Diversification](https://thequant.space/guides/risk-parity-min-variance/): The μ-free allocation family compared: what risk parity, minimum variance, and maximum diversification each optimize, the leverage that makes risk parity work, and the shared failure modes. - [Statistical Arbitrage: From Signal to Executable Portfolio](https://thequant.space/guides/statistical-arbitrage-signal-to-portfolio/): The mechanics between a stat-arb signal and an executable portfolio: neutralization, sizing, netting, turnover control, and the places the translation destroys the edge. - [Statistical Significance vs Economic Significance in Trading Research](https://thequant.space/guides/statistical-vs-economic-significance/): Why t-statistics mislead in finance: multiple testing and the factor zoo, the t > 3 hurdle, economic magnitude after costs, and the two-by-two matrix for judging any empirical result. - [Survivorship Bias in Quantitative Finance: How Dead Companies Fake Alpha](https://thequant.space/guides/survivorship-bias/): How survivorship bias inflates backtests, how large the effect is by asset class, the five-dead-tickers test for any dataset, and how to build survivorship-clean universes. - [The Deflated Sharpe Ratio, Explained with Real Numbers](https://thequant.space/guides/deflated-sharpe-ratio/): How the deflated Sharpe ratio corrects for multiple testing, fat tails, and short samples: the intuition, the formulas, and the worked example — 100 random backtests produce a Sharpe ≈ 2.5 by luck alone. - [The Most Important Quant Finance Papers, by Topic](https://thequant.space/guides/important-quant-finance-papers/): The strongest quantitative finance papers in each research area, ranked by empirical rigor and math complexity from a 5,000+ paper scored archive — updated automatically as new work is scored. - [TimescaleDB vs ClickHouse vs DuckDB vs kdb+ for Tick Data Research (2026)](https://thequant.space/guides/tick-data-databases/): An honest comparison of TimescaleDB, ClickHouse, DuckDB, QuestDB, and kdb+ for storing and querying tick data in quant research — by workload, not by benchmark marketing. - [Transaction Costs, Slippage, and Market Impact: From Paper Alpha to Tradeable Alpha](https://thequant.space/guides/transaction-costs-slippage-market-impact/): How to model transaction costs in backtests: the four cost components, the square-root impact law, honest cost ranges by asset class, and the turnover arithmetic that kills most published strategies. - [Volatility Targeting: Benefits, Hidden Leverage, and Drawdown Risk](https://thequant.space/guides/volatility-targeting/): Volatility targeting mechanics: why scaling by inverse vol has worked, the leverage it quietly embeds, gap risk and vol-spike deleveraging, and the estimator choices that change everything. - [Walk-Forward Analysis and Out-of-Sample Testing, Done Honestly](https://thequant.space/guides/walk-forward-out-of-sample-testing/): How to run out-of-sample tests that mean something: why k-fold fails on market data, purging and embargoes, anchored vs rolling walk-forward, and the evaluate-once discipline. - [What 'Robustness' Actually Means in a Trading Strategy](https://thequant.space/guides/robustness-trading-strategy/): A precise definition of strategy robustness: the five dimensions (parameters, universe, time, costs, implementation), how to test each, and how to read a paper's robustness section adversarially. - [What Is Backtest Overfitting? Definition, Detection, and Defenses](https://thequant.space/guides/backtest-overfitting/): Backtest overfitting explained: how selection among many trials fits noise, a concrete numerical demonstration, the PBO/deflated-Sharpe detection tools, and the defenses that actually work. - [What Makes a Factor Tradable?](https://thequant.space/guides/what-makes-a-factor-tradable/): The gap between a published factor premium and a tradable one: implementation costs, capacity, crowding, borrow reality, and the checklist that converts paper premia into honest expectations. - [When to Use Parquet, Postgres, or a Columnar Database](https://thequant.space/guides/parquet-vs-database/): The three storage archetypes for quant research — files, relational, columnar-analytical — matched to workload shapes, with the two-tier default and the migration triggers. - [Why a High Sharpe Ratio May Not Be Investable](https://thequant.space/guides/high-sharpe-ratio-not-investable/): Seven reasons a high backtested Sharpe ratio fails to translate into investable returns: selection, capacity, skew, leverage limits, correlation timing, costs, and career horizons. - [Why Turnover Can Destroy Factor Returns](https://thequant.space/guides/turnover-factor-returns/): The turnover arithmetic that decides factor profitability: signal decay vs trading cost, the rebalance-frequency trade-off, turnover-reduction techniques, and the reporting gap in academic papers. ## Tools Browser calculators (no server; inputs stay local). - [Deflated Sharpe Ratio & Minimum Track Record Calculator](https://thequant.space/tools/deflated-sharpe-calculator/): Interactive deflated Sharpe ratio calculator: probabilistic Sharpe ratio, expected maximum Sharpe under N zero-skill trials, DSR, and minimum track record length — with skew, kurtosis, and sample length. Runs in your browser. - [Monte Carlo Equity Curve Simulator](https://thequant.space/tools/equity-curve-simulator/): Simulate thousands of trading equity curves from win rate, reward-to-risk, and position size. Percentile bands, drawdown statistics, and risk of ruin — all in your browser. - [Quant Researcher Compute-Cost Calculator](https://thequant.space/tools/compute-cost-calculator/): Interactive calculator: local GPU vs cloud GPU vs API costs for quant research. Editable assumptions, break-even hours, and a monthly budget for your whole stack. - [Tick Data Storage Sizer & Cost Estimator](https://thequant.space/tools/tick-data-storage-sizer/): Interactive tick data storage calculator: raw and compressed GB per day, year, and total for trades, quotes, L2 depth, or full order book across your universe — with October 2026 cost estimates for NVMe, S3, ClickHouse Cloud, Timescale Cloud, QuestDB Cloud, and a Hetzner box, plus the data-feed bill. ## Reports Weekly digests and monthly "state of research" reports computed from the corpus. - [The State of Quant Finance Research, September 2026](https://thequant.space/posts/state-of-quant-research-2026-09/): 92 quant finance papers dated September 2026: topic shares, rigor and math scores, quadrant mix, code availability, and the most active authors — generated from the scored archive. - [Quant Research Weekly — October 3, 2026](https://thequant.space/posts/weekly-digest-2026-10-03/): The 10 strongest of 43 new quant finance papers this week, ranked by empirical rigor and math complexity. - [Quant Research Weekly — September 26, 2026](https://thequant.space/posts/weekly-digest-2026-09-26/): The 10 strongest of 25 new quant finance papers this week, ranked by empirical rigor and math complexity. - [Quant Research Weekly — September 19, 2026](https://thequant.space/posts/weekly-digest-2026-09-19/): The 7 strongest of 7 new quant finance papers this week, ranked by empirical rigor and math complexity. - [Quant Research Weekly — September 12, 2026](https://thequant.space/posts/weekly-digest-2026-09-12/): The 10 strongest of 14 new quant finance papers this week, ranked by empirical rigor and math complexity. - [Quant Research Weekly — September 6, 2026](https://thequant.space/posts/weekly-digest-2026-09-06/): The 10 strongest of 14 new quant finance papers this week, ranked by empirical rigor and math complexity. - [Daily Research Summary - 2026-01-25](https://thequant.space/posts/daily-research-2026_01_25/) - [Daily Research Summary - 2026-01-24](https://thequant.space/posts/daily-research-2026_01_24/) - [Daily Research Summary - 2026-01-20](https://thequant.space/posts/daily-research-2026_01_20/) - [Daily Research Summary - 2026-01-19](https://thequant.space/posts/daily-research-2026_01_19/) - [Daily Research Summary - 2026-01-18](https://thequant.space/posts/daily-research-2026_01_18/) ## Paper pages 5399 paper pages live under https://thequant.space/flowcharts// (63 with a public code link, listed at https://thequant.space/papers-with-code/). Each page contains: link to the paper, authors, abstract, the two scores and the quadrant, a one-paragraph "why this score" rationale, and a Mermaid research flowchart (goal, data, methodology, computation, findings). The page_url field in the JSON data points to the page for each paper. Author pages exist at https://thequant.space/authors// for authors with three or more papers. ## Optional - [Scoring methodology](https://thequant.space/score-guide/) - [About the site and pipeline](https://thequant.space/about/) - [Papers with code](https://thequant.space/papers-with-code/) - [Search (client-side, Pagefind)](https://thequant.space/search/) - [Dataset download page (CSV)](https://thequant.space/dataset/)