Trading and Investing — Active Trading & How Financial Markets Work (Family Root)

Trading & Investing (foundation + hub)

The front door for active trading and how financial markets actually work for a US retail participant. This skill does two jobs:

  1. Foundation — it carries the shared overview every trading sub-topic builds on: the asset classes, the market participants, primary vs secondary markets, how an order travels from your broker to the market, market sessions and hours, the core investing-vs-trading distinction, and the risk/regulatory backbone. Deep treatments live in references/ (loaded on demand).
  2. Hub / router — it routes specific questions down to the family’s spokes (see the routing table for the original 17-spoke plan, and the Foundation references table below for all 44 reference files as built). The spokes are the intended family; as each is built it owns its depth and this hub just points to it.

Educational information only — NOT financial, investment, tax, or legal advice, and NOT a recommendation to buy, sell, or hold anything. All securities and trading carry risk of loss; you can lose money, and with leverage you can lose more than you put in. Past performance does not guarantee future results. The large majority of active retail/day traders underperform a simple index or lose money (see references/investing-vs-trading.md). Rules, products, tax treatment, and the vendor landscape change — every volatile claim here is stamped as of 2026; verify current facts with the primary regulator (SEC/Investor.gov, FINRA, CFTC, SIPC) before acting. For a personal decision, consult a licensed professional.

Sibling skill — the passive/retirement seam (read this first if unsure)

This family is the active side: markets, instruments, execution, trading styles, and the risks of trading. Its sibling investing-and-retirement (in the consumer-finance family) owns the passive, long-term, retirement side — low-cost index buy-and-hold, 401(k)/403(b)/IRA/Roth mechanics and contribution limits, employer match and vesting, dollar-cost averaging for retirement, Social Security claiming, target-date defaults, and choosing a robo-advisor or fee-only fiduciary.

The two overlap on shared vocabulary (diversification, asset allocation, ETFs vs mutual funds, fees). When the intent is long-term passive wealth-building, defer to investing-and-retirement; when the intent is understanding markets or actively trading, stay here. portfolio-theory-and-asset-allocation (a spoke here) covers the theory (MPT, efficient frontier, factor models, rebalancing math); investing-and-retirement covers the consumer how-to.

Routing table — the 17 planned spokes

Spokes are the intended family. Built = reference file exists in references/ and is listed in the Foundation references table below. Unbuilt = hub answers from this foundation until the spoke is built; check the available-skills list for a standalone installed skill of that slug first.

Spoke Build state Route here when the question is about…
stock-and-equity-trading Built Market/trader side of equities: what a share is and where it trades, long/short mechanics, active-vs-passive evidence. The issuer side — valuation multiples, dividends/buybacks/splits, IPOs/SPACs/lockups — split out to equity-fundamentals-and-corporate-actions.
options-trading-and-strategies Built (also standalone skill) Listed options: calls/puts, strike/expiration, the Greeks, single-leg and multi-leg strategies (spreads, straddles, covered calls, iron condors), assignment, options pricing.
derivatives-futures-and-swaps Unbuilt Futures, forwards, and swaps (non-options derivatives): contract specs, margin/mark-to-market, hedging vs speculation, interest-rate/FX/commodity/credit swaps, the broader derivatives landscape.
technical-analysis Built Reading price/volume: chart patterns, candlesticks, trend, support/resistance, indicators (RSI, MACD, moving averages, Bollinger), Dow theory, Elliott wave. Breadth/frameworks/evidence split out to technical-analysis-breadth-frameworks-and-evidence.
trading-strategies-and-styles Unbuilt The trader’s playbook by style: position/swing/day-trading/scalping mechanics, momentum/mean-reversion/breakout/trend-following systems, entries/exits, backtesting a discretionary strategy.
algorithmic-and-quant-trading Unbuilt Systematic/automated trading: strategy coding, backtesting frameworks, execution algos, market-making/stat-arb, signals, latency, the quant research workflow.
crypto-and-digital-asset-trading Unbuilt Trading crypto on centralized venues: spot vs perps/futures, exchanges and custody, stablecoins, spot crypto ETFs, order books, on-exchange mechanics (CEX).
forex-and-currency-trading Unbuilt The FX market: currency pairs, pips/lots, leverage, carry, sessions, majors/minors/exotics, retail spot-FX mechanics and the OTC structure.
trading-risk-management Unbuilt Sizing and protecting trading capital: position sizing, stop placement, risk-per-trade, R-multiples, drawdown control, Kelly, portfolio heat, margin/leverage management as a discipline.
portfolio-theory-and-asset-allocation Unbuilt The theory: Modern Portfolio Theory, the efficient frontier, CAPM/factor models, diversification math, correlation, rebalancing, strategic vs tactical allocation.
trading-psychology-and-behavioral-finance Unbuilt The mind: discipline, fear/greed, loss aversion, the disposition effect, overconfidence, prospect theory, cognitive biases, trading-journal and routine practice.
fixed-income-and-bond-markets Unbuilt Bonds in depth: the yield curve, duration/convexity, credit spreads, Treasuries/corporates/munis, bond pricing, rate risk, the repo/money markets.
market-microstructure-and-execution Unbuilt How trades really execute: order book dynamics, maker-taker, Reg NMS internals, dark pools/ATSs, latency, slippage, TWAP/VWAP, sub-penny price improvement, SIP vs direct feeds.
defi-and-onchain-trading Built On-chain/decentralized trading: DEXs, AMMs, liquidity pools, swaps, yield/staking, MEV, bridges — the on-chain counterpart to centralized crypto. Wallets/key-security split out to self-custody-wallets-and-key-security.
ai-and-ml-for-trading Built Machine learning applied to markets: predictive models, feature engineering on market data, sentiment/alt-data, reinforcement learning for execution. Backtesting pitfalls and production systems split out to ml-backtesting-pitfalls-and-production-systems.
trading-regulation-compliance-and-taxes Unbuilt The rulebook & the IRS: SEC/FINRA/CFTC regimes, PDT/margin rules, wash sales, trader-tax-status, capital-gains mechanics for active traders, reporting, recordkeeping. Note: crypto-specific tax lots and the crypto wash-sale status question are owned by onchain-pnl-and-tax-accounting; this spoke keeps general §1091 mechanics and non-crypto trader taxation.
onchain-pnl-and-tax-accounting Built (partial) What you actually made or lost trading on-chain: realized vs unrealized P&L, the denominator/numéraire problem (a position up in SOL terms and down in USD terms), cost-basis methods, decomposing net result into price move vs fees vs borrow/funding vs slippage vs failed-transaction waste, gross vs net, self-custody wallet reconciliation — plus crypto tax lots (token-to-token swaps as taxable disposals, per-wallet basis under Rev. Proc. 2024-28, crypto wash-sale status, Form 1099-DA).

Foundation references (load on demand)

The shared foundation is split into focused references so a spoke can cross-reference one without pulling the whole hub:

Topic Covers Reference
asset-classes-and-instruments The major asset classes a retail participant can access — equities, fixed income, forex, commodities, crypto/digital assets, pooled vehicles (ETFs vs mutual vs index vs target-date), and derivatives at overview depth. What each is and how retail accesses it. references/asset-classes-and-instruments.md
market-participants-and-structure The cast (retail, institutions, market makers, broker-dealers, exchanges, clearinghouses/CCPs), what a clearinghouse does and why it cuts counterparty risk (DTCC/NSCC, OCC), settlement & the T+1 cycle, primary vs secondary markets, exchange vs OTC, and the bid-ask spread. references/market-participants-and-structure.md
order-lifecycle-and-execution How a retail order travels broker→venue→execution→clearing→settlement; order types (market/limit/stop/stop-limit) and each one’s risk; payment for order flow (PFOF) and the best-execution duty; the NBBO; the $0-commission landscape. references/order-lifecycle-and-execution.md
market-sessions-and-venues Where things trade (NYSE/Nasdaq/CME/Cboe/OTC), US equity sessions & extended hours, the 2026 move toward overnight/24-hour equity trading, how forex/crypto/futures hours differ, market holidays, and circuit breakers (market-wide + LULD). references/market-sessions-and-venues.md
strategy-backtesting-and-development-workflow The METHOD half of the strategies spoke (§5–§6). Backtesting a discretionary strategy — sample construction, in-sample vs out-of-sample, walk-forward, transaction-cost modelling, and which metrics flatter a strategy rather than test it; plus the development workflow from hypothesis through validation to sizing and monitoring, and where that process usually breaks. The strategy families themselves stay in trading-strategies-and-styles. references/strategy-backtesting-and-development-workflow.md
trading-strategies-and-styles The discretionary and semi-systematic playbook: styles by time horizon (scalping, day, swing, position trading) and the two dominant dynamics any strategy must handle — momentum (Jegadeesh-Titman 1993, cross-sectional vs time-series, momentum crashes) and mean reversion (Bollinger reversion, pairs/cointegration, half-life of reversion, when it fails); trend following and breakout (a century of trend returns, the Turtle rules, ATR-based entries, when trend fails); and how to backtest a discretionary strategy (the translation problem, data and sample-size requirements). references/trading-strategies-and-styles.md
algorithmic-and-quant-trading Systematic and quantitative trading: strategy families (stat arb, market making, momentum, mean reversion), execution algorithms (TWAP/VWAP/IS), infrastructure and latency, and §5.3 the multiple-testing problem — the degrees-of-freedom accounting the rest of this family defers to. references/algorithmic-and-quant-trading.md
trading-risk-management Sizing and protecting trading capital: risk per trade, position-sizing methods (fixed-fractional, volatility-targeted/ATR, Kelly and fractional Kelly), stop placement, drawdown measurement and risk of ruin, portfolio heat, and risk-adjusted performance metrics. references/trading-risk-management.md
crypto-and-digital-asset-trading Trading digital assets on centralized exchanges: custodial CEX platforms running central limit order books, and how that differs structurally from US equities — no Regulation NMS, no NBBO, no consolidated tape, no industry-wide spot circuit breakers, and 24/7/365 sessions. Covers CEX mechanics and order types, custody and counterparty risk, perpetual futures with funding and margin, and stablecoin mechanics. references/crypto-and-digital-asset-trading.md
forex-and-currency-trading The FX market for a retail participant: market structure and participants; currency pairs (majors, minors, exotics); reading a quote; pips and exact pip-value calculation; lot sizes; leverage and margin mechanics; P&L calculation; the carry trade and rollover rates; FX sessions and overlap timing; and transaction costs (spread vs commission). references/forex-and-currency-trading.md
investing-vs-trading The central distinction — time horizon, goal, what drives decisions, the trading-styles spectrum, active vs passive, the high-level tax angle, and the load-bearing evidence that most active traders underperform (Barber & Odean; Taiwan & Brazil day-trader studies). references/investing-vs-trading.md
trading-risks-and-protections The risk/regulatory backbone — the Pattern Day Trader rule and its 2026 replacement, margin/leverage, short selling, leveraged forex/crypto/derivatives, structural & behavioral risks, and investor protection (SIPC, BrokerCheck, the SEC mandate, the standard disclaimer). references/trading-risks-and-protections.md
stock-and-equity-trading Market side of equities (§1, §5, §6): what a share is and where it trades, order handling and single-name/ETF mechanics, long and short mechanics including the borrow, and the active-vs-passive evidence (SPIVA, Bessembinder). The issuer side (§2–§4) is split out — see equity-fundamentals-and-corporate-actions. references/stock-and-equity-trading.md
options-trading-and-strategies Listed equity/ETF/index options for a US retail participant — calls/puts, the Greeks, implied vol, and defined- vs undefined-risk strategies (spreads, straddles, condors). references/options-trading-and-strategies.md
defi-and-onchain-trading On-chain/decentralized trading: AMM x·y=k, concentrated liquidity (v3), impermanent-loss math, price impact and slippage, MEV sandwich attacks and defence (private RPC, intent protocols), CoW/UniswapX/1inch Fusion, perpetual DEXs (GMX/dYdX/Hyperliquid), and cross-chain bridges with their risk model. Wallets and key security are split out — see self-custody-wallets-and-key-security. references/defi-and-onchain-trading.md
ai-and-ml-for-trading Machine learning applied to trading, part 1: feature engineering (information bars, fractional differentiation, PIT normalization), gradient boosting with SHAP and purged CV, neural architectures, alt-data taxonomy, LLM hallucination risk, and RL for execution. Backtesting pitfalls and production systems are split out — see ml-backtesting-pitfalls-and-production-systems. references/ai-and-ml-for-trading.md
jupiter-perps-trading Trader side of Jupiter Perps — oracle-priced peer-to-pool execution, the borrow-fee model contrasted against funding rates (both sides always pay, never negative, utilization-driven), leverage and margin, and liquidation-price drift as borrow fees accrue against the position. references/jupiter-perps-trading.md
jlp-risk-profile-and-anti-patterns The risk half of the JLP position (§3–§4) — what you are actually exposed to as the counterparty to Jupiter Perps traders. Net open-interest skew (85–90% long) making JLP structurally short leverage into rallies; oracle and cheap-liquidity risk per Chaos Labs’ 2024 warnings; audit history; and the hedging trap — the Drift exploit destroyed the hedge leg (41.7M JLP, ~$155M) while the JLP leg was fine, converting market risk into counterparty risk on the hedge venue. Plus the twelve LP-side anti-patterns. references/jlp-risk-profile-and-anti-patterns.md
jupiter-perps-leverage-and-liquidation How a Jupiter Perps position is margined and how it dies (§3–§4). The 1.1x–250x range and collateral rules; why the liquidation price DRIFTS as borrow fees accrue against the position rather than sitting fixed; the 100%-of-remaining-collateral liquidation penalty; the stop-vs-liquidation race; and the trader-side anti-patterns, chief among them treating the borrow fee as a funding rate. references/jupiter-perps-leverage-and-liquidation.md
jupiter-jlp-pool LP side of Jupiter Perps — JLP composition and target weights, mint/redeem mechanics, how trader fees accrue into the virtual price, the LP-as-counterparty payoff (you are short trader P&L), and the resulting risk profile. references/jupiter-jlp-pool.md
grid-trading-strategy Grid trading as a systematic strategy, and the reporting illusion at its centre. Arithmetic vs geometric spacing, level count and width, the martingale property and why inventory accumulates against you; fee-drag arithmetic that kills over-dense grids; and the measured closed-trade illusion — a bear month logging +$95 closed P&L at a 100% win rate while the account fell 37.5%. references/grid-trading-strategy.md
jupiter-swap-routing-and-orders Jupiter’s swap/aggregation and order layer — how a route is built and split across venues, quote-vs-fill drift and slippage settings, versioned transactions and address-lookup tables, and the order products (limit, DCA, recurring) with what each actually guarantees. references/jupiter-swap-routing-and-orders.md
solana-dex-and-amm-landscape The Solana venue layer — Raydium, Orca, Meteora, Phoenix and the CLOB/AMM split; concentrated-liquidity and DLMM mechanics; how liquidity is actually fragmented across venues and what that means for a router; LP economics and impermanent loss in the Solana context. references/solana-dex-and-amm-landscape.md
sampling-frequency-and-bar-aggregation How often you sample the market — the most under-examined parameter in retail strategy design. What it controls (signal-to-noise, indicator lag, trade count and fee drag); the period × interval coupling that makes “EMA(24)” meaningless alone; aliasing and why a faster feed is not a better one; bar types (time/tick/volume/dollar); and the measured ~50pp swing on SOL from frequency alone, with hourly selected in 0 of 48 months. references/sampling-frequency-and-bar-aggregation.md
solana-oracles-pyth-switchboard The price layer underneath Solana perps, lending and liquidations. Push-vs-pull architecture and why Pyth moved to pull in V2; ~400ms publisher cadence; the confidence interval that quantifies publisher disagreement and how to gate on it; Switchboard’s customisable aggregator; who pays for the update and the staleness surface that creates; and oracle risk as it reaches a trader. references/solana-oracles-pyth-switchboard.md
solana-execution-agents-keepers-and-rfq Who actually executes your order when you do not. The keeper model and the economics deciding whether one shows up for your order — small orders can go unexecuted with the condition met, and keeper reliability degrades exactly when volatility spikes. Where keepers appear (Perps request/fulfill, Trigger, Recurring, liquidations); RFQ/market-maker flow; liquidators as adversarial keepers and the stop-vs-liquidation race. references/solana-execution-agents-keepers-and-rfq.md
trading-bot-infrastructure-and-monitoring Running a strategy as software rather than describing one. RPC/data access (websockets failing silently; your data source is a strategy parameter); state, restarts and idempotency (on-chain as single source of truth, the submitted-but-unconfirmed window, why grids are especially exposed); the live-vs-backtest divergence checklist; monitoring equity not trades; alerting on absence; and opsec for a key that moves money. references/trading-bot-infrastructure-and-monitoring.md
sleeve-weighting-and-objective-selection How much capital each sleeve gets — and first, what you are optimising for. Mean return, worst month, risk-adjusted and hit rate rank the same blends oppositely (measured: three objectives, three different winners on identical data). The scheme ladder (equal, inverse-vol, risk parity, min-variance, Kelly) ordered by how much each must estimate; why correlation-based schemes are fragile when the input swings −0.58 to +0.68; the plateau argument for round-number weights. references/sleeve-weighting-and-objective-selection.md
cross-asset-generalisation-testing Whether a result is a property of the strategy or of the one price series you tested it on. The five-rung generalisation ladder and what each rung actually rules out; why a correlated second asset falsifies well but confirms poorly, so a break is strong evidence and a pass is weak; what to hold fixed (logic, protocol) vs re-fit (parameters, via the same procedure); and parameter stability as a more informative signal than returns. references/cross-asset-generalisation-testing.md
asset-specific-vs-universal-parameters Which parameters travel between assets and which must be re-fitted. Three classes (structural / scale-dependent / asset-specific) and the test that assigns one: run the identical selection procedure on both assets and compare selected values, not returns. Normalisation converts most apparently-asset-specific parameters into structural ones. Measured: 10-level grid and 4-hourly sampling modal on both SOL and BTC, while trend’s edge flipped sign — agreeing parameters do not imply a universal edge. references/asset-specific-vs-universal-parameters.md
selection-rule-design The third walk-forward knob nobody tests — the RULE that picks a config from the training window. Five candidates (argmax mean, median, Sharpe, maximin, rank aggregation) and what each implicitly believes carries forward; the two controls that make a selection experiment interpretable — a RANDOM baseline (does selection beat blind picking?) and an ORACLE bound (how much was even available?). Measured over 48 OOS months: all five rules beat random (-1.61%) and the incumbent sits outside random’s 95% band, but captured only 29% of the random->oracle span and buy-and-hold (+5.64%) still beat every rule. The maximin paradox — selecting on the worst training month produced the WORST out-of-sample tail, because a 12-month minimum is one noisy observation. Rule and window interact, so they cannot be tuned separately. references/selection-rule-design.md
walk-forward-window-length-and-refit-cadence The two walk-forward knobs almost everyone inherits without testing: selection-window length and refit cadence. The common-OOS-window trap (a longer window leaves fewer test months, so naive comparisons confound knob with sample). Measured over 28 cells on identical months: monthly refitting was sub-optimal at 7 of 7 window lengths; the inherited window (12) was good, the inherited cadence was not; all 28 cells lost money. Two seductive sub-readings that do NOT survive scrutiny: the worst-month column is ONE month, and the raw config-switch drop reverses once normalised. references/walk-forward-window-length-and-refit-cadence.md
empirical-backtest-findings-log The measured-results log for this family — including the runs that FALSIFIED claims made elsewhere in it. The closed-trade illusion measured; two caveats this data disproved (bar fills were NOT optimistic; a trend prediction that was simply wrong); a 240-config sweep in which zero configs rescued the bear month; walk-forward quantifying selection bias (+42% in-sample → +4.24% OOS); the out-of-sample result that reversed an earlier in-sample rejection; correlation instability; the cross-asset test. references/empirical-backtest-findings-log.md
regime-detection-and-classification How to tell which regime you are in, and whether acting on that belief is worth anything. Four axes worth classifying separately (trend↔range, vol level, vol direction, liquidity); detector families — ADX/Bollinger bandwidth, Hurst and variance ratio, GARCH/MS-GARCH, Markov-switching/HMM, CUSUM/Bayesian changepoint; the lag problem — every method lags, so a filter cuts wrong-regime exposure but never prevents the transition loss. references/regime-detection-and-classification.md
strategy-failure-modes-and-synergy What breaks each strategy and indicator, and how to combine them so failures offset rather than compound. Per-strategy and per-indicator failure atlases with the observable signature of each; the convex-vs-concave payoff principle behind real diversification; a compensation matrix (failure → detector → offsetting instrument); five synergy architectures; and the anti-synergies that look diversifying but are not. references/strategy-failure-modes-and-synergy.md
onchain-pnl-and-tax-accounting Knowing what you actually made on-chain: realized vs unrealized P&L, the denominator problem (up in SOL terms, down in USD terms), cost-basis methods (FIFO/LIFO/HIFO/spec-ID), P&L attribution splitting result into price move vs fees vs borrow vs slippage vs failed-transaction waste, and crypto tax lots — token-to-token swaps as taxable disposals, per-wallet tracking, wash-sale status. references/onchain-pnl-and-tax-accounting.md
technical-analysis Reading price and volume, part 1 (§1–§7): chart types and candlestick patterns, chart patterns (H&S, double top/bottom, triangles, flags, wedges), trend analysis and trendlines, support/resistance, and the core indicators — RSI, MACD, Bollinger Bands, SMA/EMA, ATR, stochastics. Breadth, the classical frameworks and the academic evidence are split out — see technical-analysis-breadth-frameworks-and-evidence. references/technical-analysis.md
indicator-signal-implementation-and-backtesting Runnable pandas/numpy implementations of the price-derived signal families — trend/MA, momentum oscillators, volatility — with exact equations and input dependencies; plus the implementation substrate that silently breaks them: Wilder smoothing vs ewm(span=n) (5.24 RSI points, 64% more signals), warm-up convergence, one bar of look-ahead = +3.8 Sharpe, repainting, TA-Lib parity. Backtest protocol and the other three signal families are split out (see next two rows). references/indicator-signal-implementation-and-backtesting.md
signal-backtest-protocol-and-regime-evidence How to backtest ONE signal without lying to yourself, plus the cited record of when each family actually worked. Anchored vs rolling walk-forward, purging, embargo, overlapping labels; explicit cost model and break-even cost in bps; the mandatory multiple-testing battery (Reality Check, SPA, FDR, PBO, Deflated Sharpe); and the regime evidence — BLL 1992 vs Sullivan-Timmermann-White 1999 vs Bajgrowicz-Scaillet FDR, decay, the TSMOM smile. references/signal-backtest-protocol-and-regime-evidence.md
signal-pairing-volume-and-pattern-signal-sets Whether two signals are independent confirmation or one bet counted twice — runnable independence test (correlation, agreement rate, PCA effective dimensionality; six “different” indicators measured as three bets; Williams %R proven identical to Stochastic %K-100). Plus the three non-oscillator families: volume/breadth, candlesticks, and price-action structure (BOS/ChoCH, S/R zones, pivots, Fibonacci). references/signal-pairing-volume-and-pattern-signal-sets.md
technical-analysis-breadth-frameworks-and-evidence The market-internals, framework and evidence half of technical analysis (§8–§12). Volume and breadth (OBV, anchored VWAP, Volume Profile/POC, A-D line, McClellan Oscillator and Summation, Zweig Breadth Thrust); the classical frameworks (Dow Theory, Elliott Wave and the Batchelor-Ramyar critique); multi-timeframe analysis; and the empirical record — Fama, BLL 1992, Sullivan et al. 1999, Park & Irwin 2007, Lo et al. 2000, the Adaptive Market Hypothesis. references/technical-analysis-breadth-frameworks-and-evidence.md
ml-backtesting-pitfalls-and-production-systems Why an ML backtest overstates what a live model will do, and what keeps a deployed one honest. The four-mechanism look-ahead taxonomy (data leakage, point-in-time failure, survivorship, label-construction leakage); multiple testing and strategy mining (Harvey/Liu/Zhu’s 316-factor audit, the t > 3.0 threshold); PBO and Deflated Sharpe; and production concerns — IC monitoring, drift detection, retraining discipline. references/ml-backtesting-pitfalls-and-production-systems.md
equity-fundamentals-and-corporate-actions Issuer side of the equity spoke — the company behind the share. Valuation multiples and their limits (P/E, earnings yield, PEG, basic vs diluted EPS, P/B), what a company does with its capital (dividends, buybacks, splits), and how shares are first created and sold (IPOs, direct listings, SPACs, lockups). Split verbatim from stock-and-equity-trading §2–§4 with numbering preserved. references/equity-fundamentals-and-corporate-actions.md
self-custody-wallets-and-key-security Holding your own keys — the custody layer beneath on-chain trading. Browser hot wallets (MetaMask vs Rabby, pre-transaction simulation, BIP-39 derivation); hardware wallets (Ledger Secure Element vs Trezor open-source firmware, the Ledger Recover controversy, blind-signing risk on complex DeFi calldata); seed-backup practice; and token-approval hygiene. references/self-custody-wallets-and-key-security.md

How to answer from this hub (output contract)

When answering directly from this foundation: (1) for any advice-seeking question, lead with the educational-only / not-advice framing and that trading carries risk of loss; (2) cite the primary regulator (SEC/Investor.gov, FINRA, CFTC, SIPC) for load-bearing facts, and stamp volatile facts as of 2026 with a “verify current” pointer; (3) when the question belongs to a spoke whose reference file exists in the Foundation references table above, Read its references/<slug>.md and answer from it — if a standalone installed skill also exists for that spoke, the standalone skill takes precedence; if the Read fails or the reference is silent on the specific question, degrade to foundation depth; (4) when the question belongs to a spoke not yet in the Foundation references table, first check the available-skills list for a standalone installed skill of that spoke’s slug — if found, invoke it; otherwise name the destination spoke and answer at foundation depth; (5) when a query spans two spokes, identify the primary spoke (the one that owns the action) and answer from it, citing the secondary spoke for supplementary detail. Keep US-centric, flagging where a fact (T+1, PDT, SIPC, Reg NMS, CFTC caps) is US-only.

First, the account (the gate before anything else)

Everything downstream runs through a brokerage account opened with a broker-dealer (after identity verification, funded from a bank). The one account choice that gates the rest is cash vs margin: a cash account trades only settled funds (no borrowing); a margin account lets the broker lend you money against the account as collateral — which is what enables short selling and triggers the margin and Pattern-Day-Trader rules in references/trading-risks-and-protections.md. Account type (individual/joint/retirement-wrapper) sits on top of that; a retirement-wrapper account points to the passive sibling, investing-and-retirement.

The one-paragraph foundation (if you only read the hub)

A retail participant buys and sells instruments (equities, bonds, FX, commodities, crypto, pooled funds like ETFs/mutual funds, and derivatives) through a broker-dealer, which routes the order to an execution venue (an exchange like NYSE/Nasdaq, a wholesaler/market maker, or an ATS); the trade then clears through a central counterparty (NSCC for US equities, OCC for listed options) and settles the next business day (T+1, as of 2026). The issuer raises money only once, in the primary market (an IPO or new issue); everything after is the secondary market, where investors trade among themselves and prices are discovered. Investing means holding for the long term to build wealth through compounding; trading means buying and selling frequently to profit from price moves — and the evidence is strong that most active retail traders underperform or lose money, so trading capital should be money you can afford to lose. Markets are regulated (SEC, FINRA, CFTC) and your brokerage account is protected against broker failure by SIPC — but nothing protects you against market losses.

Cross-cutting notes

as of 2026 volatile claims to re-verify (detail in the references) — one line each, highest-stakes first:

Other seams:

References

Each foundation reference carries its own cited ## References / ## Sources section anchored to primary authorities — SEC / Investor.gov, FINRA, CFTC, SIPC, DTCC, OCC, the Federal Reserve / BIS, S&P Dow Jones Indices (SPIVA), and the peer-reviewed household-finance literature (Barber & Odean; the Taiwan and Brazil day-trader studies). Volatile facts are dated as of 2026 with “verify current” pointers. Start from the relevant reference above; as further spokes are built, each will carry its own deeper citations.

Cross-hub map — where every trading-and-investing topic lives

All active-trading material lives under this single hub. Built reference files are listed below; for unbuilt spokes, check whether a standalone installed skill exists for that slug and invoke it, or answer at foundation depth.

Hub Owns Example reference files
trading-and-investing Trading & Investing — all active trading, markets, and risk topics references/asset-classes-and-instruments.md, references/market-participants-and-structure.md, references/order-lifecycle-and-execution.md, references/market-sessions-and-venues.md, references/investing-vs-trading.md, references/trading-risks-and-protections.md, references/stock-and-equity-trading.md, references/options-trading-and-strategies.md, references/defi-and-onchain-trading.md, references/ai-and-ml-for-trading.md, references/technical-analysis.md, references/jupiter-perps-trading.md, references/jupiter-jlp-pool.md, references/grid-trading-strategy.md, references/onchain-pnl-and-tax-accounting.md, references/indicator-signal-implementation-and-backtesting.md, references/strategy-failure-modes-and-synergy.md, references/regime-detection-and-classification.md, references/sleeve-weighting-and-objective-selection.md, references/cross-asset-generalisation-testing.md, references/asset-specific-vs-universal-parameters.md, references/walk-forward-window-length-and-refit-cadence.md, references/selection-rule-design.md, references/strategy-backtesting-and-development-workflow.md, references/jlp-risk-profile-and-anti-patterns.md, references/jupiter-perps-leverage-and-liquidation.md, references/signal-backtest-protocol-and-regime-evidence.md, references/signal-pairing-volume-and-pattern-signal-sets.md, references/equity-fundamentals-and-corporate-actions.md, references/technical-analysis-breadth-frameworks-and-evidence.md, references/ml-backtesting-pitfalls-and-production-systems.md, references/self-custody-wallets-and-key-security.md, references/empirical-backtest-findings-log.md, references/sampling-frequency-and-bar-aggregation.md, references/solana-oracles-pyth-switchboard.md, references/solana-execution-agents-keepers-and-rfq.md, references/trading-bot-infrastructure-and-monitoring.md, references/solana-dex-and-amm-landscape.md, references/jupiter-swap-routing-and-orders.md