brainbytes-dev/everything-claude-trading

# Live Trading Monitoring

name: live-trading-monitoring
description: Live trading monitoring — drift detection, performance tracking, kill switches.
origin: ECT

## When to Activate

- User is transitioning a strategy from backtest to live trading
- Designing live monitoring dashboards and alerting systems
- Building kill switches and automated risk controls
- Detecting strategy drift (live performance deviating from backtest expectations)
- Setting up daily reconciliation and P&L tracking processes

## First Questions

# Monte Carlo Simulation for Strategy Evaluation

## When to Activate
- Estimating confidence intervals for strategy performance metrics (Sharpe, drawdown, CAGR)
- Assessing ruin probability and worst-case drawdown distributions
- Stress testing strategy robustness through path simulation
- Evaluating parameter sensitivity via randomized perturbation
- Building realistic performance expectations beyond single-path backtests

## Core Concepts

### Why Monte Carlo?

**Problem with Single-Path Backtests:**
- A backtest is ONE realization of a stochastic process

# Overfitting Prevention

## When to Activate
- Evaluating whether a backtest result reflects genuine alpha or data mining artifacts
- Applying statistical tests to adjust for multiple hypothesis testing
- Implementing cross-validation methods designed for financial time series
- Assessing the minimum backtest length and statistical significance of strategy performance
- Distinguishing between robust strategies and curve-fitted illusions

## Core Concepts

### What Is Overfitting in Trading?

**Definition:** A strategy is overfit when it captures noise (random patterns) in historical data rather than signal (persistent patterns), resulting in poor live performance despite excellent backtest results.

# Research Notebook Best Practices

## When to Activate
- Setting up Jupyter/research notebook workflows for quantitative research
- Ensuring reproducibility in strategy research and backtesting
- Designing visualization standards for research output
- Organizing research pipelines from data ingestion through signal generation to evaluation
- Implementing version control and documentation standards for research code

## Core Concepts

### Research Notebook Structure

**Standard Notebook Template:**
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# Strategy Evaluation Framework

## When to Activate
- Conducting comprehensive evaluation of a trading strategy before deployment
- Adjusting Sharpe ratios for autocorrelation, non-normality, and multiple testing
- Assessing parameter stability and regime robustness
- Estimating strategy capacity and transaction cost sensitivity
- Making go/no-go decisions on strategy deployment

## Core Concepts

### Strategy Evaluation Checklist

**Five Pillars of Strategy Evaluation:**
```

# Walk-Forward Optimization

## When to Activate
- Validating trading strategy parameters using robust out-of-sample testing
- Designing in-sample/out-of-sample split methodologies for backtests
- Evaluating parameter stability across different time periods
- Preventing overfitting through structured walk-forward analysis
- Comparing anchored vs rolling window approaches for strategy calibration

## Core Concepts

### In-Sample vs Out-of-Sample

**In-Sample (IS):**
- Data period used to fit, train, or optimize strategy parameters

# Cross-Chain and CEX-DEX Arbitrage

## When to Activate
- Identifying and evaluating arbitrage opportunities between centralized and decentralized exchanges
- Analyzing cross-chain price discrepancies and bridge-based arbitrage
- Designing triangular or multi-hop arbitrage strategies
- Assessing execution risk, latency, gas costs, and bridge risks for arbitrage operations
- Building or evaluating atomic arbitrage strategies using flash loans

## Core Concepts

### CEX-DEX Arbitrage

**Price Discrepancy Sources:**
- CEX prices update via order book; DEX prices update via AMM formula on each trade

# Crypto Derivatives

## When to Activate
- Analyzing perpetual swap mechanics, funding rates, and basis trading opportunities
- Evaluating crypto futures across venues (CME, Deribit, Binance)
- Designing funding rate arbitrage or basis trading strategies
- Assessing crypto options strategies and volatility trading
- Understanding liquidation cascades and their market impact

## Core Concepts

### Perpetual Swaps

**Mechanics:**
- No expiry date — positions can be held indefinitely

# DeFi Protocol Analysis

## When to Activate
- Analyzing DeFi protocol mechanics for trading or yield opportunities
- Evaluating AMM designs (Uniswap v3, Curve), lending protocols (Aave, Compound), or staking systems
- Assessing liquidity provision strategies, including concentrated liquidity and stableswap invariants
- Comparing liquid staking solutions (Lido, Rocket Pool) or evaluating validator economics

## Core Concepts

### Automated Market Makers (AMMs)

**Constant Product (Uniswap v2):**
- Invariant: x * y = k
- Price impact is proportional to trade size relative to pool depth

# MEV Strategies

## When to Activate
- Analyzing Maximal Extractable Value (MEV) opportunities or risks in DeFi transactions
- Understanding frontrunning, sandwich attacks, and DEX arbitrage mechanics
- Evaluating MEV mitigation strategies (Flashbots, MEV-boost, private mempools)
- Designing transactions to minimize MEV extraction or to participate in MEV capture
- Assessing proposer-builder separation (PBS) and its implications for protocol design

## Core Concepts

### MEV Taxonomy

**Definition:** MEV is the maximum value that can be extracted from block production beyond the standard block reward and gas fees, by including, excluding, or reordering transactions within a block.

# On-Chain Analytics

## When to Activate
- Analyzing blockchain data for trading signals (whale movements, exchange flows, network health)
- Evaluating crypto asset fundamentals using on-chain metrics
- Building or interpreting dashboards from Glassnode, Nansen, Dune Analytics, or similar platforms
- Assessing market structure through wallet clustering and entity behavior

## Core Concepts

### Blockchain Data Layers
1. **Raw transaction data** — inputs, outputs, gas, timestamps, contract interactions
2. **Derived metrics** — aggregated statistics computed from raw data (active addresses, transfer volume)
3. **Entity-level analytics** — clustering addresses into wallets, labeling exchanges, funds, whales
4. **Behavioral signals** — interpreting entity actions as bullish/bearish indicators

# Stablecoin Analysis

## When to Activate
- Evaluating stablecoin peg mechanisms, reserve compositions, and risk profiles
- Assessing depeg risk factors and historical depeg events
- Analyzing stablecoin yield strategies and their risk/return characteristics
- Comparing stablecoin designs (fiat-backed, crypto-backed, algorithmic)
- Monitoring stablecoin market dynamics as macro indicators for crypto markets

## Core Concepts

### Stablecoin Classification

**Fiat-Backed (Custodial):**
- **USDT (Tether)** — largest by market cap; reserves include cash, T-bills, commercial paper (reduced), secured loans

# Tokenomics Analysis

## When to Activate
- Evaluating a token's supply dynamics, emission schedule, and value accrual mechanisms
- Assessing vesting unlock schedules and their potential market impact
- Analyzing inflation/deflation mechanics and burn mechanisms
- Comparing governance token designs and fee distribution models
- Due diligence on new token launches, airdrops, or protocol incentive programs

## Core Concepts

### Supply Framework

**Supply Definitions:**
- **Max supply** — hard cap on total tokens that will ever exist (BTC: 21M, not all tokens have a cap)

# Yield Farming Analysis

## When to Activate
- Evaluating DeFi yield opportunities across protocols and chains
- Calculating true APY/APR including impermanent loss and compounding effects
- Assessing risk-adjusted returns for liquidity provision strategies
- Comparing real yield (from protocol revenue) vs emission-subsidized yield
- Designing LP position management strategies including entry, exit, and rebalancing

## Core Concepts

### APY vs APR

**APR (Annual Percentage Rate):**
- Simple interest rate without compounding

# Alternative Data

name: alternative-data
description: Alternative data for trading — satellite, credit card, web traffic, app usage.
origin: ECT

## When to Activate

- User wants to incorporate alternative data into trading strategies
- Evaluating alternative data vendors or datasets
- Building signals from satellite imagery, credit card data, or web traffic
- Assessing alpha potential, coverage, and cost of alternative data sources
- Designing an alternative data evaluation framework

## First Questions

# Financial Data Quality

name: data-quality
description: Financial data quality — survivorship bias, look-ahead bias, point-in-time.
origin: ECT

## When to Activate

- User is building a backtest and needs to ensure data integrity
- Evaluating data for survivorship bias, look-ahead bias, or other distortions
- Setting up point-in-time databases for fundamental data
- Designing data cleaning and validation procedures
- Diagnosing suspicious backtest results that may stem from data issues

## First Questions

# Financial Data Vendors

name: data-vendors
description: Financial data vendors — Bloomberg, Refinitiv, Polygon, Alpha Vantage.
origin: ECT

## When to Activate

- User needs to choose a financial data provider for a project
- Comparing data vendors by coverage, quality, cost, and API design
- Setting up data ingestion pipelines from vendor APIs
- Evaluating data quality differences across providers
- Understanding vendor-specific data formats and conventions

## First Questions

# Feature Engineering for Financial ML

name: feature-engineering
description: Feature engineering for financial ML — lag features, rolling stats, ranks.
origin: ECT

## When to Activate

- User is building ML models for financial prediction
- Constructing features from price, volume, fundamental, or alternative data
- Applying cross-sectional standardization or time-series transformations
- Performing feature selection to reduce dimensionality and multicollinearity
- Designing feature pipelines that respect temporal constraints (no leakage)

## First Questions

# Sentiment Analysis for Trading

name: sentiment-analysis
description: Sentiment analysis for trading — news NLP, social media, earnings calls.
origin: ECT

## When to Activate

- User wants to build sentiment-based trading signals from text data
- Applying NLP to financial news, earnings calls, or social media
- Evaluating FinBERT or other financial language models for alpha generation
- Constructing sentiment indices or contrarian sentiment signals
- Analyzing earnings call tone for forward return prediction

## First Questions

# Web Scraping for Financial Data

name: web-scraping-finance
description: Web scraping for financial data — SEC filings, earnings, macro releases.
origin: ECT

## When to Activate

- User wants to collect financial data from web sources programmatically
- Scraping SEC EDGAR filings (10-K, 10-Q, 8-K, 13F, DEF 14A)
- Building automated pipelines for earnings data or economic calendar events
- Understanding legal, ethical, and technical considerations for financial scraping
- Setting up rate-limited, respectful data collection systems

## First Questions

# Binomial Option Pricing

> CRR model, multi-step trees, American options, early exercise boundary, calibration, and convergence to BSM.

## When to Activate

- User pricing American options where closed-form solutions do not exist
- Teaching or explaining option pricing intuition through discrete models
- Pricing options with discrete dividends or complex exercise features
- Calibrating tree parameters to match market prices
- Understanding early exercise boundaries for American puts and calls on dividend-paying stocks
- Validating other pricing methods (Monte Carlo, finite differences) against tree results

## Core Concepts

# Black-Scholes-Merton Model and Extensions

> BSM derivation intuition, formula, put-call parity, Greeks, limitations, and extensions for dividends, American options, and implied volatility.

## When to Activate

- User pricing European options or computing implied volatility
- Deriving or interpreting BSM Greeks for hedging
- Understanding BSM assumptions and when they break down
- Extending BSM for dividends, discrete hedging, or jumps
- Computing put-call parity relationships or identifying arbitrage
- Teaching or explaining option pricing fundamentals

## Core Concepts

# Credit Derivatives

name: credit-derivatives
description: Credit derivatives — CDS, CDO, credit indices, default correlation.
origin: ECT

## When to Activate

- User wants to price or trade credit default swaps (CDS)
- Working with credit indices (CDX, iTraxx)
- Analyzing CDO tranches and default correlation
- Building credit curve models or estimating default probabilities
- Evaluating CDS-bond basis trades or credit relative value

## First Questions

# Exotic Options

> Barriers, Asians, lookbacks, digitals, cliquets, autocallables, and quanto options. Pricing, hedging, and risk management.

## When to Activate

- User pricing or hedging barrier options (knock-in, knock-out)
- Pricing Asian options (arithmetic or geometric average)
- Analyzing lookback, digital/binary, or cliquet options
- Understanding autocallable structures and their risks
- Quanto options and cross-currency derivative pricing
- Hedging exotic payoffs and understanding Greeks behavior

## Core Concepts

# Derivatives Hedging Strategies

name: hedging-strategies
description: Derivatives hedging — delta-neutral, gamma scalping, vega hedging.
origin: ECT

## When to Activate

- User wants to hedge a derivatives position or portfolio
- Designing delta-neutral strategies with gamma scalping
- Managing vega exposure across a vol surface
- Building portfolio hedges using options or futures
- Analyzing cross-hedging and basis risk
- Understanding hedge accounting requirements

# Interest Rate Derivatives

> Swaps, swaptions, caps/floors, curve construction, OIS discounting, and multi-curve framework.

## When to Activate

- User pricing interest rate swaps or computing swap rates
- Constructing yield curves from market instruments (bootstrapping)
- Pricing swaptions, caps, or floors
- Implementing multi-curve framework with OIS discounting
- Understanding convexity adjustments for non-standard rate products
- Analyzing interest rate risk and DV01/duration for derivatives portfolios

## Core Concepts

# Monte Carlo Methods for Derivatives Pricing

> Random number generation, GBM simulation, path-dependent options, variance reduction, Longstaff-Schwartz for Americans, and convergence analysis.

## When to Activate

- User pricing path-dependent or exotic derivatives (Asian, barrier, lookback)
- Pricing options on multiple underlyings (basket, rainbow, worst-of)
- American option pricing via Longstaff-Schwartz regression
- Evaluating variance reduction techniques for pricing efficiency
- Multi-factor model simulation (stochastic vol, rates, credit)
- Convergence analysis and error estimation for MC prices

## Core Concepts

# Structured Products

name: structured-products
description: Structured products — autocallables, reverse convertibles, capital protected.
origin: ECT

## When to Activate

- User wants to understand or price structured products
- Analyzing autocallable notes, reverse convertibles, or capital-protected products
- Decomposing a structured product into its embedded options
- Evaluating structured product risk from an investor or issuer perspective
- Building payoff diagrams or pricing structured notes

## First Questions

# Volatility Surface Construction and Analysis

> Implied vol surface, smile/skew, SVI parameterization, SABR model, sticky strike vs sticky delta, surface dynamics, and arbitrage constraints.

## When to Activate

- User constructing or calibrating an implied volatility surface
- Analyzing volatility smile, skew, or term structure patterns
- Choosing between SVI, SABR, or other surface models
- Understanding sticky strike vs sticky delta dynamics for hedging
- Checking a vol surface for arbitrage violations (butterfly, calendar spread)
- Interpolating or extrapolating implied volatilities for unlisted strikes/expiries

## Core Concepts

# Volatility Trading

name: vol-trading
description: Volatility trading — straddles, variance swaps, dispersion, VIX.
origin: ECT

## When to Activate

- User wants to trade volatility as an asset class
- Analyzing realized vs implied volatility and the vol risk premium
- Pricing or trading variance swaps and vol swaps
- Designing dispersion trades (index vol vs single-stock vol)
- Trading VIX futures, options, or VIX-related products
- Building systematic vol selling strategies with tail protection

# Execution Algorithms

> Execution algorithms — TWAP, VWAP, IS, POV, Iceberg for optimal trade execution.

## When to Activate

- User needs to execute large orders with minimal market impact
- Choosing between execution algorithm types (benchmark, scheduled, opportunistic)
- Implementing TWAP, VWAP, or implementation shortfall algorithms
- Designing iceberg or hidden order strategies
- Building an algo selection framework based on order characteristics

## Core Concepts

### Why Execution Algorithms?

# Latency Optimization

> Low-latency trading infrastructure — co-location, FPGA, kernel bypass, network optimization.

## When to Activate

- User is building or evaluating low-latency trading systems
- Optimizing tick-to-trade latency for market making or arbitrage
- Understanding co-location, FPGA-based trading, kernel bypass networking
- Measuring and profiling latency in trading systems
- Evaluating hardware and network topology for trading infrastructure

## Core Concepts

### Why Latency Matters

# Market Microstructure Theory

> Market microstructure theory — limit order books, price discovery, bid-ask spread economics.

## When to Activate

- User needs to understand how prices form in electronic markets
- Analyzing bid-ask spread components (adverse selection, inventory, order processing)
- Modeling limit order book dynamics and depth
- Understanding informed vs uninformed trading (Kyle, Glosten-Milgrom models)
- Evaluating market quality metrics (spread, depth, resilience, price efficiency)

## Core Concepts

### What Is Microstructure?

# Order Flow Analysis

> Order flow analysis — volume profile, footprint charts, delta for short-term price prediction.

## When to Activate

- User is analyzing intraday order flow for trading signals
- Building volume profile analysis (POC, value area, single prints)
- Using cumulative delta and footprint charts for directional bias
- Identifying aggressor imbalances and absorption patterns
- Predicting short-term price moves from order flow data

## Core Concepts

### What Is Order Flow?

# Trading Performance Metrics

> Sharpe, Sortino, Calmar, Information Ratio, and comprehensive performance measurement.

## When to Activate

- User needs to evaluate or compare trading strategy performance
- Computing risk-adjusted return metrics (Sharpe, Sortino, Calmar, IR)
- Analyzing drawdown characteristics and recovery periods
- Understanding return calculation methodologies (arithmetic, geometric, annualization)
- Applying Sharpe ratio haircuts for backtest overfitting

## Core Concepts

### Return Calculation Foundations

# Smart Order Routing

> Smart order routing — dark pools, lit markets, internalization, and best execution.

## When to Activate

- User needs to route orders across multiple venues for best execution
- Evaluating dark pool strategies and hidden liquidity
- Understanding Reg NMS, MiFID II best execution requirements
- Building or evaluating a smart order routing engine
- Analyzing venue selection and latency considerations

## Core Concepts

### Fragmented Markets

# Tick Data Processing

> Tick data processing — cleaning, aggregation, bar construction from raw market data.

## When to Activate

- User is working with raw tick-level trade and quote data
- Cleaning tick data (outliers, splits, corporate actions, bad prints)
- Constructing bars from ticks (time, volume, dollar, tick, entropy bars)
- Handling bid-ask bounce and trade classification
- Designing data storage and retrieval for large tick datasets

## Core Concepts

### What Is Tick Data?

# Transaction Cost Analysis

> Transaction cost analysis (TCA) — slippage, market impact, benchmarks for measuring execution quality.

## When to Activate

- User needs to measure and decompose execution costs
- Evaluating broker or algorithm performance
- Building market impact models (Almgren-Chriss, square-root model)
- Comparing execution against benchmarks (VWAP, arrival price, close)
- Conducting pre-trade cost estimation for portfolio rebalancing

## Core Concepts

### What TCA Measures

# Central Bank Analysis

## When to Activate
- Parsing central bank communications (statements, minutes, press conferences, speeches)
- Analyzing dot plots, SEP projections, and forward guidance
- Evaluating quantitative easing/tightening and balance sheet operations
- Comparing policy stances across central banks (Fed, ECB, BOJ, BOE, PBOC)
- Trading around central bank meetings and policy decisions
- Understanding Taylor Rule and policy rule frameworks

## Core Concepts

### Federal Reserve (FOMC)

**Decision Framework:**

# Commodities Fundamental Analysis

## When to Activate
- Analyzing commodity futures term structure (contango, backwardation) and roll yield
- Evaluating supply-demand balances for energy, metals, or agricultural commodities
- Understanding seasonal patterns and their trading implications
- Assessing inventory data, OPEC dynamics, and production economics
- Building commodity-based macro views or trading strategies

## Core Concepts

### Futures Term Structure

**Contango:**
- Longer-dated futures > near-term futures > spot price

# Economic Indicators for Trading

## When to Activate
- Interpreting economic data releases and their market implications
- Building nowcasting models or tracking economic momentum
- Trading around data releases (NFP, CPI, PMI, GDP)
- Understanding leading, coincident, and lagging indicator frameworks
- Analyzing surprise indices and market reaction patterns

## Core Concepts

### Indicator Classification

**Leading Indicators (predict future economic activity):**
- **PMI (Purchasing Managers' Index)** — survey-based, 50 = expansion/contraction threshold

# FX Fundamental Analysis

## When to Activate
- Analyzing currency valuation using fundamental models (PPP, interest rate parity, balance of payments)
- Evaluating central bank policy divergence and its impact on exchange rates
- Assessing terms of trade shifts, current account dynamics, and capital flows
- Understanding FX intervention mechanics and their effectiveness
- Building medium-to-long term FX views based on macro fundamentals

## Core Concepts

### Purchasing Power Parity (PPP)

**Absolute PPP:**
- Exchange rate should equalize price levels: S = P_domestic / P_foreign

# Geopolitical Risk for Trading

## When to Activate
- Assessing the market impact of geopolitical events (conflicts, sanctions, elections)
- Building scenario analysis frameworks for geopolitical uncertainty
- Trading supply chain disruptions caused by geopolitical events
- Evaluating safe haven flows and war premiums in commodities
- Analyzing sanctions regimes and their impact on markets and capital flows

## Core Concepts

### Geopolitical Risk Measurement

**GPR Index (Caldara & Iacoviello):**
- Text-based index counting geopolitical risk references in major newspapers

# Yield Curve Analysis

## When to Activate
- Analyzing term structure of interest rates for trading signals or macro assessment
- Designing curve trades (steepeners, flatteners, butterflies)
- Interpreting yield curve inversions and their economic implications
- Calculating key rate durations and understanding curve risk
- Evaluating central bank policy impact on different parts of the curve

## Core Concepts

### Yield Curve Shapes

**Normal (Upward Sloping):**
- Short rates < long rates

# Alternative Allocations

> Alternative investment allocation — PE, VC, hedge funds, real assets in portfolio context.

## When to Activate

- User is allocating to private equity, venture capital, hedge funds, or real assets
- Modeling illiquidity premiums and J-curve effects
- Building commitment pacing models for PE/VC programs
- Evaluating hedge fund strategies for portfolio diversification
- Integrating alternatives into a traditional stock/bond portfolio

## Core Concepts

### Why Alternatives?

# Benchmark Tracking

> Index tracking and replication strategies — full replication, sampling, and optimization.

## When to Activate

- User is building an index-tracking portfolio or ETF
- Minimizing tracking error against a benchmark with fewer securities
- Understanding ETF creation/redemption mechanics
- Handling index reconstitution and rebalancing events
- Evaluating tracking error sources and budgeting

## Core Concepts

### Replication Methods

# Black-Litterman Model

> Black-Litterman model for portfolio allocation — combining equilibrium returns with investor views.

## When to Activate

- User wants to incorporate subjective or model-driven views into portfolio optimization
- Standard MVO produces extreme or unintuitive weights and the user needs a better starting point
- Building a tactical overlay on top of a strategic benchmark allocation
- Combining quantitative signals with an equilibrium prior
- Blending views of different confidence levels into a single allocation

## Core Concepts

### The Problem BL Solves

# Factor Investing

> Factor investing and smart beta strategies — systematic exposure to return drivers.

## When to Activate

- User is building factor-based portfolios (value, momentum, quality, low-vol, size)
- Designing smart beta strategies or evaluating factor ETFs
- Analyzing factor crowding, timing, or cycle dynamics
- Constructing multi-factor portfolios and managing factor interactions
- Evaluating implementation costs and capacity constraints of factor strategies

## Core Concepts

### What Are Factors?

# Markowitz Optimization

> Mean-variance optimization and efficient frontier construction for portfolio allocation.

## When to Activate

- User needs to construct an optimal portfolio from a universe of assets
- Building efficient frontiers or finding minimum-variance portfolios
- Implementing constrained portfolio optimization (long-only, sector limits, turnover)
- Dealing with estimation error in expected returns and covariance matrices
- Comparing robust optimization techniques (shrinkage, resampling, Bayesian)

## Core Concepts

### Mean-Variance Optimization (MVO)

# Portfolio Performance Attribution

> Decomposing portfolio returns into allocation, selection, and factor contributions.

## When to Activate

- User needs to explain why a portfolio outperformed or underperformed its benchmark
- Implementing Brinson-Fachler attribution for equity portfolios
- Building factor-based attribution using regression or risk models
- Performing fixed income attribution (duration, credit, curve)
- Linking single-period attribution across multiple periods

## Core Concepts

### What Is Attribution?

# Rebalancing Strategies

> Portfolio rebalancing methodologies — when, how, and how much to trade.

## When to Activate

- User needs to decide how often and by what rules to rebalance a portfolio
- Comparing calendar vs threshold vs tactical rebalancing
- Minimizing rebalancing costs (transaction costs, taxes, market impact)
- Implementing tax-aware rebalancing with tax-loss harvesting
- Analyzing the rebalancing premium and drift tolerance

## Core Concepts

### Why Rebalance?

# Risk Parity

> Risk parity and equal risk contribution portfolios — balancing risk, not capital.

## When to Activate

- User wants a portfolio where each asset contributes equally to total risk
- Building an All Weather-style portfolio across asset classes
- Implementing Hierarchical Risk Parity (HRP) as an alternative to MVO
- Designing leveraged risk parity strategies
- Comparing risk-based allocation methods (ERC, inverse volatility, minimum variance)

## Core Concepts

### Why Risk Parity?

# Alpha Research

name: alpha-research
description: Alpha signal discovery, testing, and evaluation. Use when researching new trading signals.

## When to Activate

- User is exploring new predictive signals for asset returns
- Evaluating whether a trading idea has genuine predictive power
- Combining multiple alpha signals into a composite
- Diagnosing why an alpha signal is decaying
- Designing a research pipeline for systematic signal discovery
- Applying multiple testing corrections to a signal library

## First Questions

# Bayesian Methods

name: bayesian-methods
description: Bayesian inference for trading — prior selection, posterior updating, Bayesian portfolio optimization. Use for probabilistic trading analysis.

## When to Activate

- User wants to incorporate prior beliefs into parameter estimation
- Estimating Sharpe ratios or expected returns with uncertainty quantification
- Building Black-Litterman portfolios with investor views
- Working with small sample sizes where frequentist methods are unreliable
- Combining multiple data sources or expert opinions with market data
- Performing robust parameter estimation under uncertainty

## First Questions

# Deep Learning for Trading

name: deep-learning-trading
description: Deep learning for trading — LSTM, transformers, sequence models for price prediction. Use when applying neural networks to financial data.

## When to Activate

- User wants to apply neural networks to financial time series prediction
- Building sequence models (LSTM, Transformer) for return or volatility forecasting
- Exploring attention mechanisms for capturing temporal dependencies
- Implementing deep learning for limit order book or high-frequency data
- Evaluating transfer learning or pre-trained models for financial applications

## First Questions

# Factor Models

name: factor-models
description: Factor model construction and analysis — Fama-French, Carhart, Barra, custom factors. Use when building or analyzing factor-based strategies.

## When to Activate

- User wants to build a factor-based investment strategy
- Decomposing portfolio returns into systematic risk exposures
- Constructing custom factors from fundamental or technical data
- Evaluating factor premia or factor crowding risk
- Building a Barra-style risk model for portfolio construction
- Comparing single-factor (CAPM) to multi-factor frameworks

## First Questions

# Kalman Filters

name: kalman-filters
description: Kalman filter applications in finance — dynamic beta estimation, pairs trading, state-space models. Use for dynamic parameter estimation.

## When to Activate

- User needs dynamically estimated parameters (betas, hedge ratios) that evolve over time
- Building pairs trading strategies with adaptive hedge ratios
- Modeling unobserved state variables (e.g., true value, latent factors)
- Tracking time-varying relationships between assets
- Smoothing noisy financial signals without excessive lag

## First Questions

# Machine Learning for Finance

name: ml-for-finance
description: Machine learning for trading — supervised models, feature importance, cross-validation for time series. Use when applying ML to trading problems.

## When to Activate

- User wants to apply supervised ML models to predict returns, classify regimes, or rank assets
- Designing cross-validation schemes for financial time series
- Evaluating feature importance for trading signals
- Building ensemble models from multiple alpha signals
- Diagnosing model overfitting or decay in production ML trading systems

## First Questions

# Regime Detection

name: regime-detection
description: Market regime detection using HMM, Markov switching, and structural break methods. Use when identifying market environments.

## When to Activate

- User wants to classify market environments (bull/bear/sideways, high/low volatility)
- Adapting strategy parameters to current market conditions
- Building regime-dependent allocation or risk management rules
- Detecting structural changes in market dynamics
- Evaluating whether a strategy works differently across regimes

## First Questions

# Reinforcement Learning for Trading

name: reinforcement-learning-trading
description: Reinforcement learning for trading — Q-learning, policy gradient, reward shaping for portfolio management. Use when exploring RL-based trading systems.

## When to Activate

- User wants to apply reinforcement learning to portfolio management or execution
- Designing reward functions for trading agents
- Formulating trading as a Markov Decision Process
- Evaluating RL vs traditional optimization for dynamic allocation
- Building simulation environments for training trading agents

## First Questions

# Signal Processing

name: signal-processing
description: Signal processing for financial data — wavelets, Fourier analysis, noise filtering. Use when extracting signals from noisy financial data.

## When to Activate

- User wants to decompose price series into trend, cycle, and noise components
- Detecting dominant cycles or periodicities in financial data
- Denoising financial signals while preserving important features
- Extracting multi-scale information (short-term noise vs long-term trend)
- Filtering price data for cleaner technical indicators
- Analyzing frequency-domain properties of returns

## First Questions

# Time Series Analysis

name: time-series-analysis
description: Time series analysis for financial data — ARIMA, GARCH, cointegration, stationarity. Use when modeling financial time series.

## When to Activate

- User needs to model or forecast financial time series (prices, returns, volatility)
- Testing for stationarity before applying statistical models
- Modeling conditional volatility (GARCH family)
- Testing for cointegration between two or more assets
- Diagnosing autocorrelation, seasonality, or structural breaks in data
- Building econometric models for trading signals

## First Questions

# Correlation Risk Analysis

> Regime-dependent correlation, diversification failure, dynamic correlation models, and correlation trading strategies.

## When to Activate

- User analyzes portfolio diversification and correlation assumptions
- Correlation breakdown during market stress events
- Dynamic correlation modeling with DCC-GARCH or similar
- Correlation trading strategies (dispersion, correlation swaps)
- Risk model validation where correlation assumptions drive results
- Multi-asset portfolio construction sensitive to correlation estimates

## Core Concepts

# Counterparty Credit Risk

> Margin, collateral, CVA/DVA/FVA, central clearing, netting, wrong-way risk, and credit support framework.

## When to Activate

- User evaluating counterparty exposure in OTC derivative portfolios
- Computing CVA, DVA, or FVA for derivative pricing
- Designing collateral agreements (CSA) and margin frameworks
- Assessing the impact of central clearing mandates
- Analyzing wrong-way risk in derivative portfolios
- Managing bilateral vs. cleared counterparty risk

## Core Concepts

# Drawdown Analysis and Management

> Max drawdown, recovery analysis, conditional drawdown at risk, stop-loss design, de-leveraging rules, and drawdown-based position sizing.

## When to Activate

- User analyzing drawdown characteristics of a strategy or portfolio
- Designing stop-loss or de-leveraging rules
- Evaluating whether a drawdown is within expected bounds or signals strategy failure
- Position sizing based on maximum acceptable drawdown
- Comparing strategies using drawdown-adjusted performance metrics
- Setting risk limits expressed as drawdown thresholds

## Core Concepts

# Expected Shortfall (CVaR) and Tail Risk Measurement

> Expected Shortfall calculation, advantages over VaR, regulatory adoption under FRTB, extreme value theory, and tail dependence modeling.

## When to Activate

- User needs a coherent risk measure that captures tail losses beyond VaR
- Regulatory capital computation under FRTB (Fundamental Review of the Trading Book)
- Portfolio optimization with tail risk constraints
- Analyzing extreme loss scenarios and tail dependence between assets
- Comparing ES across portfolios for risk budgeting
- Stress testing that requires understanding the full tail distribution

## Core Concepts

# Options Greeks Management

> Delta, gamma, vega, theta hedging and higher-order Greeks. Portfolio-level risk management, dynamic hedging, and rebalancing strategies.

## When to Activate

- User manages an options portfolio and needs to hedge or analyze risk
- Delta-neutral portfolio construction or adjustment
- Gamma scalping strategies or gamma exposure analysis
- Vega hedging for volatility-sensitive positions
- Theta decay analysis and time decay management
- Higher-order Greeks (vanna, volga, charm) for exotic or large positions
- Understanding P&L attribution through Greeks decomposition

## Core Concepts

# Liquidity Risk Assessment

> Bid-ask spreads, market impact, Amihud illiquidity, liquidity-adjusted VaR, and the illiquidity premium.

## When to Activate

- User evaluating the liquidity profile of a portfolio or individual positions
- Estimating market impact costs for large trades
- Computing liquidity-adjusted risk measures (LVaR)
- Assessing liquidity risk during stress periods
- Analyzing the illiquidity premium in asset pricing
- Designing execution strategies that minimize market impact

## Core Concepts

# Position Sizing Methodologies

> Kelly criterion, optimal-f, fixed fractional, volatility targeting, risk parity position sizing, and practical implementation.

## When to Activate

- User designing position sizing rules for a trading strategy
- Applying Kelly criterion or fractional Kelly to determine bet size
- Volatility-based position sizing for systematic strategies
- Risk budgeting and risk parity allocation across strategies or assets
- Evaluating the tradeoff between position size, expected return, and drawdown risk
- Comparing position sizing methods for a given strategy profile

## Core Concepts

# Stress Testing Methodologies

> Historical, hypothetical, and reverse stress testing. Scenario design, regulatory frameworks, crisis replay, and integration with risk management.

## When to Activate

- User needs to design stress test scenarios for a portfolio or trading desk
- Regulatory stress testing requirements (CCAR, DFAST, EBA, PRA)
- Evaluating portfolio resilience to specific market dislocations
- Reverse stress testing to identify scenarios that cause business failure
- Post-crisis analysis and scenario library construction
- Board-level risk reporting requiring scenario-based loss estimates

## Core Concepts

# Tail Risk Hedging Strategies

> Put spreads, VIX calls, tail risk parity, cost-effective crash protection, and systematic tail hedging programs.

## When to Activate

- User wants to protect a portfolio against extreme market declines
- Evaluating cost-effective hedging instruments for left-tail events
- Designing a systematic tail risk hedging program with defined budget
- Analyzing Universa-style or tail risk parity approaches
- Comparing hedging instruments: puts, VIX, CDS, trend-following overlays
- Assessing the drag of tail hedging on long-term portfolio returns

## Core Concepts

# Value at Risk Models

> Parametric, historical simulation, and Monte Carlo VaR. Confidence levels, holding periods, backtesting, regulatory requirements, and known limitations.

## When to Activate

- User asks about VaR calculation for a portfolio or single position
- Risk reporting requires VaR numbers at specific confidence levels
- Backtesting existing VaR models for accuracy
- Regulatory capital calculations under Basel III/IV
- Comparing VaR methodologies for a given portfolio composition
- Evaluating holding period assumptions for different asset classes

## Core Concepts

# Carry Strategies

name: carry-strategies
description: Carry strategies across asset classes — FX carry, yield curve carry, commodity roll.
origin: ECT

## When to Activate

- User wants to build a carry-based systematic strategy
- Designing FX carry trades (long high-yield, short low-yield currencies)
- Analyzing yield curve roll-down and carry in fixed income
- Evaluating commodity roll yield as a return source
- Building multi-asset carry portfolios with tail risk hedging

## First Questions

# Event-Driven Trading

name: event-driven
description: Event-driven trading — earnings, M&A arb, index rebalancing, spin-offs.
origin: ECT

## When to Activate

- User wants to trade around corporate events (earnings, M&A, spin-offs)
- Designing a merger arbitrage or event arbitrage strategy
- Evaluating index rebalancing trades (additions/deletions)
- Analyzing post-earnings announcement drift (PEAD)
- Building an event study framework for backtesting catalysts

## First Questions

# Market Making

name: market-making
description: Market making strategies — bid-ask management, inventory risk, Avellaneda-Stoikov framework. Use for market-making strategy design.

## When to Activate

- User wants to design a market-making strategy
- Understanding bid-ask spread management and inventory risk
- Implementing the Avellaneda-Stoikov optimal quoting framework
- Analyzing order book dynamics and adverse selection
- Setting risk limits and position bounds for market makers

## First Questions

# Mean Reversion

name: mean-reversion
description: Mean reversion strategies — Bollinger, Z-score, Ornstein-Uhlenbeck, and statistical approaches. Use when designing mean-reversion systems.

## When to Activate

- User wants to trade assets that revert to a mean or equilibrium level
- Building pairs trading or spread trading strategies
- Using Bollinger Bands, Z-scores, or OU models for entry/exit
- Estimating half-life of mean reversion
- Distinguishing between mean-reverting and trending instruments

## First Questions

# Momentum Strategies

name: momentum-strategies
description: Momentum strategy design — time-series, cross-sectional, and dual momentum approaches. Use when building momentum-based systems.

## When to Activate

- User wants to build a momentum-based trading strategy
- Choosing between time-series and cross-sectional momentum
- Designing lookback periods, rebalancing frequency, or volatility scaling
- Understanding momentum crashes and how to mitigate them
- Combining momentum with other factors or signals

## First Questions

# Multi-Asset Systematic Strategies

name: multi-asset-strategies
description: Multi-asset systematic strategies — risk premia, CTA, global macro systematic.
origin: ECT

## When to Activate

- User wants to build a multi-asset systematic portfolio
- Designing risk premia harvesting strategies across asset classes
- Building CTA / managed futures trend-following systems
- Implementing systematic global macro strategies
- Constructing a portfolio of uncorrelated strategies

## First Questions

# Pairs Trading

name: pairs-trading
description: Pairs trading methodology — cointegration, distance, copula methods. Use when designing market-neutral pairs strategies.

## When to Activate

- User wants to trade the spread between two related securities
- Selecting pairs using cointegration, correlation, or fundamental linkage
- Constructing and monitoring spread positions
- Managing risk in market-neutral pair portfolios
- Evaluating pair stability and when to exit broken pairs

## First Questions

# Relative Value Strategies

name: relative-value
description: Relative value strategies — cross-asset, capital structure, convertible arb.
origin: ECT

## When to Activate

- User wants to trade price dislocations between related instruments
- Designing capital structure arbitrage (equity vs credit vs CDS)
- Building convertible bond arbitrage strategies
- Trading basis between cash and derivatives (bond basis, CDS-bond basis)
- Analyzing on-the-run vs off-the-run Treasury spread opportunities

## First Questions

# Statistical Arbitrage

name: stat-arb
description: Statistical arbitrage — PCA-based, factor-neutral, sector-neutral portfolio construction. Use for stat-arb strategy development.

## When to Activate

- User wants to build a statistical arbitrage portfolio
- Decomposing returns using PCA to extract residual alpha
- Constructing sector-neutral or factor-neutral portfolios
- Trading mean reversion of residual returns
- Estimating capacity and managing risk of stat-arb strategies

## First Questions

# Trend Following

name: trend-following
description: Trend-following strategies — CTA-style, moving averages, breakout systems, Turtle Trading. Use when designing trend-based systems.

## When to Activate

- User wants to build a systematic trend-following or CTA-style strategy
- Designing moving average crossover or channel breakout systems
- Implementing Turtle Trading rules or similar mechanical systems
- Position sizing with ATR or volatility-based methods
- Constructing multi-asset trend portfolios across futures markets

## First Questions