RESEARCH BRIEF: Building an automated short-term trading system on a sub-$25k account OBJECTIVE I'm...

RESEARCH BRIEF: Building an automated short-term trading system on a sub-$25k account OBJECTIVE I'm building a personal automated trading system that connects TradingView (signal generation via Pine Script + webhook alerts) to Alpaca (execution via API), for my own account only. Research the best practices, evidence-based strategies, failure modes, and current-as-of-2026 facts I need to build this well and not lose money to avoidable mistakes. Throughout, distinguish empirically-supported findings from trading folklore, and explicitly flag anything that may have changed recently so I know to verify it against current primary sources. MY CONSTRAINTS - Account equity is under $25,000. - Broker is Alpaca (US equities + crypto; no options or futures). Signal platform is TradingView. - This is for my own capital; I am not managing money for others. - My original goal was high-frequency stock scalping, but I understand the Pattern Day Trader rule likely makes that infeasible on a sub-$25k margin account. DESIGN DECISIONS I'VE TENTATIVELY MADE — please VALIDATE or CHALLENGE each with evidence: 1. Because of PDT, pivot away from intraday stock scalping toward either (Track A) intraday crypto scalping on Alpaca, or (Track B) liquid equity/ETF momentum held hours-to-days to avoid the day-trade definition. Default to Track B. 2. Signal stack: EMA trend pair + MACD + VWAP + ATR-scaled stops/targets + an overextension filter (RSI or Bollinger) + a session/time-of-day gate + a higher-timeframe index (SPY/QQQ) regime filter. 3. Instruments: liquid large-caps and major ETFs with tight spreads; avoid low-float small-cap momentum names for an automated system. 4. Risk: small fixed-fractional risk per trade, a hard daily-loss kill switch, position size capped relative to the instrument's average daily volume. 5. Execution architecture: TradingView-as-brain initially (alerts set to "Once Per Bar Close"), with the signal source designed to be swappable to a code-based brain later; protective exits implemented as broker-side bracket/OCO orders rather than a second webhook. Tell me where any of these is wrong, suboptimal, or risky. === PRIORITY 1: THE PDT FORK (resolve this first) === - Confirm the current PDT rule: exact day-trade threshold, the rolling-window definition, the equity minimum, what "day trade" includes (incl. partial closes), consequences of being flagged, and whether/how flags can be removed. - Margin vs. cash account tradeoffs under $25k. For a cash account, explain T+1 settlement mechanics, "good faith violations," free-riding, and how settled-vs-unsettled funds limit same-day round-trips — i.e., whether a cash account is actually a workaround for frequent trading or just a different cage. - Which asset classes are PDT-exempt (crypto, futures, forex) and which Alpaca actually supports. - For Track A (crypto on Alpaca): current crypto trading fees, typical spreads on major pairs, and a realistic assessment of whether a short-term/scalping strategy can produce positive expectancy AFTER fees + spread + slippage. This is the decisive question for Track A — find any data, studies, or credible practitioner analysis on crypto scalping net profitability. - For Track B (equities held overnight+): confirm overnight holds don't count as day trades; quantify the gap-risk tradeoff. - Compare all viable paths (throttle to PDT limits / Track A crypto / Track B swing / cash-account settlement / fund to $25k) and recommend which best preserves a "scalping-like" goal for an automated system on a small account. === REGULATION, ACCOUNTS, TAXES === - Tax treatment of frequent trading: short-term capital gains, the wash-sale rule and how it bites high-frequency equity traders (disallowed losses, phantom gains), whether crypto is currently subject to wash-sale rules, and the Trader Tax Status / IRC §475(f) mark-to-market election — eligibility, benefits, and whether it's worth it at this scale. - Record-keeping requirements for taxes and any compliance considerations for running a personal trading bot. === STRATEGY & EDGE VALIDATION === - Which short-term strategies have documented positive expectancy vs. which are folklore: momentum/breakout, opening-range breakout, VWAP reversion, mean-reversion, pullback-to-EMA, etc. Cover both equities (Track B horizon) and crypto (Track A horizon). - The indicator multicollinearity problem: EMA, MACD, and Bollinger are all price-derived and correlated. How do I combine genuinely non-redundant signals (price + volume + volatility + time + market breadth)? Which indicator combinations have real evidence behind them? - Strategy/alpha decay: why edges stop working and how to detect decay early. - Benchmarking: how to honestly determine whether a strategy beats buy-and-hold and the risk-free rate after all costs and effort. === BACKTESTING DONE RIGHT (and its traps) === - Repainting and lookahead bias in TradingView/Pine in depth: what causes it, how to write non-repainting scripts, the barstate pitfalls, the historical-vs-realtime calculation difference, and how to verify a strategy isn't repainting. - Proper methodology: in-sample/out-of-sample splits, walk-forward analysis, parameter-sensitivity/robustness testing, Monte Carlo on trade sequence, minimum sample sizes, and regime-dependence testing (trend vs. chop, high vs. low volatility). - Realistic transaction-cost modeling in backtests: spread, slippage, fees, market impact, and why assuming you get the bar's close price is wrong. - Overfitting/curve-fitting: how to recognize it and how much backtest performance to discount for it. - Key metrics and how to read them: expectancy, profit factor, Sharpe/Sortino, max drawdown, win rate vs. reward:risk, and risk of ruin. === MARKET MICROSTRUCTURE & EXECUTION COSTS === - Bid/ask spread and NBBO; why data-feed coverage matters (Alpaca free tier is IEX-only vs. full SIP via the paid plan) and how that affects spread reads and fills for short-term trading. Is the paid SIP data effectively mandatory here? - Slippage: causes, how to measure it, how to model it, and how to minimize it (order-type choice, liquidity). - Liquidity and market impact: how position size relative to average volume drives slippage; criteria for selecting tradeable instruments (spread, volume, volatility, halt frequency, borrow availability). - Payment-for-order-flow and Alpaca execution quality / price improvement — what fill quality to realistically expect. - Halts, LULD limit-up/limit-down, circuit breakers, and how stop orders behave through halts and gaps (a stop is not a guaranteed price). - Short selling specifics: locate/borrow availability, hard-to-borrow fees, short-sale restrictions (SSR/uptick), and how shorting works on Alpaca. - Pre/post-market trading characteristics. === ORDER TYPES & EXECUTION TACTICS === - Deep comparison of order types for short-term trading: market, limit, marketable-limit, stop, stop-limit, bracket/OCO/OTO, IOC/FOK/MOO/MOC — when to use each and the tradeoff between fill certainty and price control. - Best practices for attaching protective stops/targets atomically at entry (bracket/OCO) so exits never depend on a second signal arriving. === SYSTEM ARCHITECTURE & ENGINEERING === - TradingView-webhook-to-broker bridge patterns; existing open-source bridges/projects and the specific pitfalls people hit with them. - Webhook security: a webhook URL is effectively a bearer credential — best practices for shared-secret/HMAC validation, IP-allowlisting TradingView's published ranges, replay protection, and never transmitting API keys in alert messages. - Reliability of the signal path: TradingView's alert-firing latency (the reported 1–5s batch delay after bar close), alert reliability/outages, alert-count limits by TradingView plan tier, and how all of this constrains the viable holding period. At what frequency does TV-as-brain stop being viable and code-as-brain (computing indicators from the broker's own data stream) become necessary? - Idempotency, deduplication, stale-signal rejection, signal ordering, per-symbol locking/concurrency. - State management and reconciliation: treating the broker as source of truth, detecting and recovering from state drift, reconciling on startup after a crash, and persistence. - Order-lifecycle handling: partial fills, rejects, cancels, and consuming the broker's trade-update WebSocket stream. - Alpaca API specifics to verify: current rate limits for trading vs. market-data endpoints (sources conflict between ~200/min and stricter trading-endpoint limits), paper-trading environment fidelity and its differences from live, and WebSocket streaming details. - Reliability engineering: handling broker/TV outages, retries with backoff, 429 rate-limit handling, fail-safe defaults (do nothing or flatten under uncertainty), health checks, auto-restart/process supervision, monitoring, and phone alerting. - Hosting choices (VPS vs. serverless) for an always-on bot, secrets management, least-privilege and paper/live key separation, and clock/timezone/DST handling for US market hours. - Testing strategy: unit tests, integration tests against paper, replaying historical signals, and deliberately testing the failure cases (duplicate signal, stale signal, partial fill, broker reject, mid-trade outage, crash-and-restart-while-in-position, spread blowout). === RISK MANAGEMENT & CAPITAL SURVIVAL === - Position sizing methods: fixed-fractional, volatility/ATR-based, Kelly and why fractional-Kelly; why retail traders systematically over-leverage. - Setting a per-trade risk %, a daily-loss limit / kill switch, and max concurrent positions that actually preserve capital across a losing streak; include risk-of-ruin and drawdown-recovery math (e.g., the gain needed to recover a given drawdown). - Stop-loss design: hard vs. ATR vs. time-based vs. trailing stops; gap risk; overnight risk specifically for Track B.