Guides · 33 how-to's
Guides
Task-first how-to's for the work quants and AI-in-markets engineers actually do. Each guide lists prerequisites, walks the steps, links the tool that does the heavy lifting, and names the mistakes that quietly ruin the result.
Risk & portfolio construction
8 guides-
How to Backtest a Value-at-Risk Model
Backtest a value-at-risk model: count breaches, run the Kupiec frequency test and the Christoffersen independence test, and read clustered breaches.
8 MIN READ
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How to Build an Efficient Frontier
Build an efficient frontier: estimate expected returns and the covariance matrix, solve minimum-variance portfolios, and handle estimation error.
8 MIN READ
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How to Calibrate Probability Forecasts
Calibrate probability forecasts: score with Brier and log loss, read a reliability diagram, apply Platt or isotonic recalibration, confirm the fix.
8 MIN READ
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How to Choose a Risk-Adjusted Return Metric
Choose a risk-adjusted return metric by matching what it penalizes to the risk you care about: Sharpe, Sortino, Calmar, Omega, information ratio.
8 MIN READ
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How to Choose Between Sharpe and Sortino
Choose between Sharpe and Sortino by matching the metric to your returns: Sharpe for symmetric returns, Sortino when downside is the concern.
7 MIN READ
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How to Estimate Drawdown Recovery Time
Estimate drawdown recovery time from a strategy's return moments: run a Cornish-Fisher Monte Carlo, read the recovery percentiles, plan for the tail.
8 MIN READ
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How to Read Options Greeks
Read the options Greeks in plain terms: delta, gamma, theta, vega, and rho, what each measures, how they interact, and how to manage a position.
8 MIN READ
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How to Size Positions with Kelly Under Uncertainty
Size positions with the Kelly criterion when your edge is uncertain: estimate edge and variance, apply fractional Kelly, and stress-test the drawdown.
9 MIN READ
Backtesting & validation
6 guides-
How to Avoid Backtest Overfitting
Avoid backtest overfitting with a practical workflow: limit trials, hold out data, measure the probability of overfitting, prefer robust rules.
8 MIN READ
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How to Compute a Deflated Sharpe Ratio
Compute a deflated Sharpe ratio step by step: gather the inputs, count trials, adjust for skew and kurtosis, and read the probability the edge is real.
8 MIN READ
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How to Generate Synthetic Market Data for Testing
Generate synthetic market data for testing: choose a process that captures fat tails and volatility clustering, then use it without overfitting.
8 MIN READ
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How to Run Walk-Forward Validation
Run walk-forward validation: choose anchored or rolling windows, set fit and test lengths, re-optimize per window, and aggregate out-of-sample results.
8 MIN READ
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How to Test a Pair for Cointegration
Test a pair for cointegration with Engle-Granger: estimate the hedge ratio, run an ADF test on the residual, measure the half-life, validate it.
9 MIN READ
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How to Validate a Trading Strategy
A step-by-step process for validating a trading strategy without fooling yourself: out-of-sample testing, trial accounting, deflated Sharpe, and a VaR backtest.
8 MIN READ
Market microstructure
2 guides-
How to Backtest with Realistic Slippage
Backtest with realistic slippage: model the spread, temporary and permanent market impact, latency, and partial fills so paper returns survive live.
9 MIN READ
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How to Choose a Market Data Vendor
Choose a market data vendor by matching coverage, resolution, and history to your strategy, then comparing total cost of ownership across vendors.
9 MIN READ
AI in markets
17 guides-
How to Audit a Research Prompt for Look-Ahead Leakage
Audit an LLM research prompt for look-ahead leakage: scan for outcome-revealing prices, directional words, and future facts the model could cheat with.
8 MIN READ
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How to Build a RAG Pipeline Over SEC Filings
Build a RAG pipeline over SEC filings: ingest and chunk 10-Ks, embed and retrieve passages, ground answers with citations, and verify extracted numbers.
10 MIN READ
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How to Build a Regression Suite for a Finance Prompt
Build a regression suite for a finance prompt: collect representative and adversarial cases, define expected outputs, and run it on every change.
8 MIN READ
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How to Choose Between RAG and Fine-Tuning for Filings
Choose between RAG and fine-tuning for filings: match the method to whether the task needs fresh knowledge or a fixed format, then weigh the cost.
9 MIN READ
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How to Cut LLM Token Cost in a Finance Agent
Cut LLM token cost in a finance agent: right-size the model, cache the stable prefix, trim context, batch deferrable work, measure cost per decision.
9 MIN READ
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How to Decide Between Batch and Real-Time LLM Calls
Decide between batch and real-time LLM calls by deadline: route latency-tolerant jobs to batch for the discount, keep waiting-on work real-time.
7 MIN READ
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How to Defend a Finance LLM Against Prompt Injection
Defend a finance LLM against prompt injection: treat retrieved content as untrusted, separate instructions from data, restrict tools, test attacks.
8 MIN READ
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How to Deploy an LLM in a Finance Pipeline
A practical guide to putting a large language model into a finance workflow safely: scoping the task, grounding answers, guarding inputs, and verifying every number.
9 MIN READ
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How to Design a Fallback Chain for LLM Providers
Design a fallback chain for LLM providers: order models by quality and cost, set timeouts and retries, handle rate limits, and simulate failures first.
8 MIN READ
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How to Detect Hallucinations in Finance LLM Output
Detect hallucinations in finance LLM output: verify every number against the source, check citation faithfulness, and flag unsupported claims.
8 MIN READ
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How to Estimate the Cost of an AI Research Agent
Estimate the cost of an AI research agent: count tokens per step, multiply by tool calls and retries, scale to markets per day, and add a cost cap.
9 MIN READ
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How to Evaluate an LLM for 10-K Extraction
Evaluate an LLM for 10-K extraction: build a labeled gold set, score field accuracy and faithfulness, test edge cases, weigh cost against quality.
9 MIN READ
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How to Get Structured Output from a Finance LLM
Get reliable structured output from a finance LLM: define a strict schema, constrain generation, validate every field, and verify the numbers.
8 MIN READ
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How to Select an LLM for a Finance Task
Select an LLM for a finance task by defining task, latency, cost, context, and quality constraints, then ranking on your own eval, not benchmarks.
8 MIN READ
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How to Set Up Prompt Caching for Finance
Set up prompt caching for finance LLM workloads: identify the stable prefix, order the prompt for cache hits, manage the window, measure savings.
8 MIN READ
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How to Summarize Earnings Calls with an LLM
Summarize earnings calls with an LLM reliably: scope the summary, ground it in the transcript with citations, verify figures, estimate the cost.
8 MIN READ
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How to Test an Agent Skill for a Finance Task
Test an agent skill for a finance task: define the skill, run representative and adversarial inputs, score extraction, and measure cost and latency.
8 MIN READ
Adjacent surfaces