#title: "Systematic Trading: A Foundational Reference" date: 2026-05-20 status: living-document

#Systematic Trading

A reference for building a Jane-Street-style systematic trading operation at retail scale. Focused on programmatic execution of quantitative strategies, not discretionary trading. Written for the operator who already programs and now wants to convert that into a private wealth engine.

#1. Strategy Archetypes Suited to a Mathematician

The honest filter: at retail capital, most "edge" strategies real shops run (latency arb, true HFT market making, capacity-heavy stat arb on the full Russell 3000) are unavailable. The remaining surface is narrower but real. Mathematical strength matters more on the research side (signal discovery, risk modeling) than the execution side.

Feasibility by capital tier:

• $5k: Crypto market making on Coinbase/Hyperliquid, micro futures (1 MES contract), or swing factor strategies on equities. Avoid PDT entirely by using cash account or futures.
• $25k: Margin equities unlocked. Pairs trading, ETF mean reversion, options spreads (Level 3). Still under capacity ceiling for most factor strategies.
• $100k: Real multi-strategy portfolio. Run 3-5 uncorrelated strategies. Begin meaningful options vol selling (cash-secured puts, defined-risk spreads). Consider LLC.
• $500k: Approaching prop-shop territory. Trader Tax Status worth filing. Can absorb fixed costs of pro data ($500-2000/mo Databento, dedicated VPS), employ leverage carefully.

The big trap: at retail capital, the strategies with the highest theoretical Sharpe (HFT market making, latency arb) are also the ones you cannot run. The strategies you can run (medium-frequency mean reversion, trend, factors) are the ones the literature has been chewing on for 25 years and where edge is thin. Plan around capacity-tolerant, low-frequency edge with strong risk management, not against high-Sharpe fantasy.

#2. Asset Classes

Two regulatory facts to internalize:

• Section 1256 (futures + broad-based index options + cash-settled options on indices) offers 60/40 tax treatment regardless of holding period. Blended top rate ~26.8% vs. 37% for STCG equities. This alone is a 10 percentage-point edge before any alpha. Futures are also exempt from the PDT rule and the wash sale rule.
• Wash sale rule (Section 1091) applies to substantially identical securities within 30 days. Algorithmic equity strategies that re-enter the same name constantly will produce a wash sale nightmare unless you elect Section 475(f) mark-to-market (which requires Trader Tax Status).

The PDT change (2026): The SEC approved elimination of the $25k pattern day trader rule effective June 4, 2026, replacing it with a risk-based margin framework: no minimum balance for cash-equivalent traders, $2k or maintenance margin (whichever greater) for leveraged. This materially expands what's runnable under $25k. (Britannica explainer, NerdWallet)

#3. Programmatic Access — Broker APIs

#public.com

Docs | Changelog

• Assets: Equities, ETFs, options, index options, crypto, corporate bonds, treasuries (bonds added March 2026).
• Auth: OAuth-style API keys per the Individual API Program Terms.
• Endpoints: REST; supports PUT to amend orders without cancel/replace (Feb 2026 update).
• Free, no commissions on stocks/ETFs/options contracts.
• SDKs: Official Python library.
• Strengths: Free, bond+treasury support is unusual for retail APIs, modern REST design.
• Weaknesses: Newer than Alpaca/IBKR — community is small, fewer Stack Overflow answers, paper trading less mature.

#Mercury Bank API

Docs | Treasury endpoint

Mercury is banking, not brokerage. For trading purposes it enables:

• Programmatic ACH / wire / RTP / check / virtual card.
• Real-time balance + statement reconciliation across operating accounts and Mercury Treasury (MMF-style sweep).
• Webhooks for transactions over a threshold, low-balance alerts.
• Treasury endpoint exposes yield-bearing account data (currently ~4-5% APY on idle cash).

How this composes with trading: keep operating capital in Mercury Treasury (yielding) → programmatically ACH to broker when buying power needed → ACH back to Mercury when flat. The yield differential between idle broker cash and Mercury Treasury on $100k+ is meaningful (typically 100-200 bps).

#Alternatives

#Crypto

For market making in crypto specifically, Hummingbot ships connectors for most of the above. CCXT (ccxt/ccxt, ~33k stars) is the universal abstraction layer if you need to swap exchanges.

#4. Backtesting Frameworks & Libraries

Ranked, with current state as of mid-2026:

Recommended workflow: VectorBT for parameter sweeps and signal discovery → NautilusTrader for execution-realistic backtest → live deploy on NautilusTrader or broker-specific code. Avoid the temptation to live-trade directly from a vectorized research notebook.

#Data Sources for Backtesting

yfinance is fine for prototyping and research. It is unfit for production-grade backtests. Use Norgate or Polygon for serious work. Databento if you need L2 / tick.

#5. Similar GitHub Projects Worth Studying

Curated, with stars and current status (May 2026 snapshot):

#6. Hardware Requirements

Latency-sensitive (HFT): not realistic for retail. Co-located NY4 servers ($2000-10000/mo), kernel-bypass networking, FPGA NICs. Don't bother.

Mid-frequency (sub-second to minute bars): the current i7-265K + 30GB desktop is overkill for execution. The dGPU is irrelevant for non-ML strategies.

Overnight / swing / daily bars: cheapest VPS imaginable suffices. A $5 Hetzner CX11 will run a daily-bar strategy.

Research and backtesting:

Recommended split for this setup:

• moirai (desktop, i7-265K, 30GB, RX 6800 XT): research + backtest + Jupyter.
• STARGATE (server, Arch, 916GB): live execution under systemd + Postgres/TimescaleDB for tick storage + Grafana for monitoring.
• Optional NY4 VPS later if you actually find a latency-sensitive edge.

#7. Cloud VPS / Colocation

Recommendation by stage:

• Today through $100k AUM: STARGATE on home network is fine. Add Hetzner Ashburn ($5/mo) as failover.
• $100k-$500k or any sub-second strategy: QuantVPS NY4 Lite ($42-129/mo).
• $500k+ or genuine HFT: Beeks dedicated in NY4 with broker cross-connect.

#8. Capital Staging Plan

Prop firm shortcut: instead of personal capital you can lease buying power from a prop firm. Realistic ones:

Prop firms are useful as leverage on a tested edge. They are not useful as a substitute for an edge. Eval rules (max drawdown, daily loss limits, news restrictions) bias toward conservative, low-risk strategies, sometimes punishing real edge.

#9. Risk Management & Infrastructure

#Position Sizing

• Fixed fractional: bet f of equity per trade. Simple, robust. f = 0.01-0.02 typical.
• Volatility-targeted: size positions so each contributes equal $-vol. Compute position = target_vol * equity / (px * realized_vol). Rob Carver's bread-and-butter.
• Kelly fraction: f* = (bp - q) / b. The textbook answer. In practice, use half-Kelly or less because the inputs are estimated and overestimation is fatal.
• Risk parity / mean-variance: for multi-strategy portfolios. Use cvxpy or riskfolio-lib.

#Stops, Limits, Kill Switches

• Per-trade stop: hard limit on loss per position.
• Daily loss limit: pause all strategies after X% drawdown. Mandatory.
• Strategy kill switch: any individual strategy auto-disables on N consecutive losers or M% drawdown over K days.
• Position concentration cap: no single position > P% of equity.
• Correlation cap: total exposure to correlated bucket (e.g. tech beta) capped.

#Monitoring Stack

For STARGATE deployment:

#Validation

• Walk-forward optimization (not single-pass in-sample optimization). Lopez de Prado's Combinatorial Purged Cross-Validation is the gold standard.
• Deflated Sharpe Ratio (DSR) accounts for backtest overfitting by penalizing the number of trials. (Bailey & Lopez de Prado paper)
• Out-of-sample minimum: hold back 25% of the data span. Don't peek.
• Synthetic data testing: generate paths under a realistic process (GBM with regime switching, or block-bootstrap), check the strategy's distribution of returns. If edge collapses on random data, edge isn't real.
• Trade randomization: shuffle trade entry times by ±5 bars. If the strategy is timing-fragile, that's overfit.
• Capacity test: re-run backtest with realistic slippage (10-50 bps per side depending on size and liquidity). Many "edges" die here.

#10. Learning Path

#Books — Read in This Order

1. Ernie Chan, Quantitative Trading (2nd ed.). The gentle on-ramp. (Amazon)
2. Ernie Chan, Algorithmic Trading: Winning Strategies and Their Rationale. Mean reversion, momentum, options. Worked examples in MATLAB but easily ported.
3. Ernie Chan, Machine Trading. ML-flavored extension.
4. Rob Carver, Systematic Trading and Leveraged Trading. The best practical books for futures portfolio construction. Companion code: pysystemtrade. (Carver's blog)
5. Marcos Lopez de Prado, Advances in Financial Machine Learning. (Amazon) Hard. Worth it. Chapters 7 (cross-validation) and 11 (deflated Sharpe) are essential reading even if you skip the rest.
6. Lopez de Prado, Machine Learning for Asset Managers. Shorter follow-up.
7. Rishi Narang, Inside the Black Box (2nd ed.). How real quant funds are organized.
8. David Aronson, Evidence-Based Technical Analysis. The book that kills naïve TA via proper statistical hypothesis testing.
9. Andrew Pole, Statistical Arbitrage. The pairs/stat-arb bible.
10. Sheldon Natenberg, Option Volatility and Pricing. Vol surface intuition.
11. Euan Sinclair, Volatility Trading and Option Trading. Practitioner-grade options.
12. Stefan Jansen, Machine Learning for Algorithmic Trading (2nd ed.). Use as a reference together with the GitHub repo.

#Papers and Reading Lists

• arxiv-q-fin — daily preprints, especially q-fin.ST (statistical finance) and q-fin.TR (trading and microstructure).
• SSRN Financial Economics Network — where the practitioners publish.
• Moskowitz, Ooi, Pedersen, Time Series Momentum (2012) — foundational trend paper.
• Avellaneda & Stoikov, High-frequency trading in a limit order book (2008) — market making bedrock.
• Lopez de Prado, The 7 Reasons Most Machine Learning Funds Fail — read this before you build anything ML.
• Harvey, Liu, Zhu, ...and the Cross-Section of Expected Returns (2016) — why most published factors are noise.

#Courses

• CQF (Certificate in Quantitative Finance) — heavy, 6 months, ~$20k. Useful as forcing function and credential.
• Coursera — Financial Engineering and Risk Management (Columbia) — solid free option.
• QuantConnect Bootcamp — free, framework-anchored.
• Hudson & Thames courses — companions to mlfinlab.

#Forums

• r/algotrading — quality mixed; goldmine for broker pain-points.
• Wilmott Forum — old-guard quant board; still active.
• Quantopian Forum archive — Quantopian shut down in 2020; the archive is research gold.
• EliteTrader — practitioner-heavy.
• QuantConnect community — anchored to LEAN.

#11. Tax & Legal (US)

Caveat: reference, not advice. At $50k+ engage a quant-specialized CPA — Green Trader Tax is the most-cited.

#Trader Tax Status (TTS)

To qualify, you typically need to demonstrate:

• Sustained, frequent trading (case law suggests ≥4 days/week, ~720+ trades/year, average holding period <31 days).
• Hours: significant time spent.
• Material trading: substantial $ relative to other income.
• Intent to derive income from trading.

Benefits: deduct trading expenses (data, computers, home office, education) on Schedule C; deduct margin interest as business expense.

#Section 475(f) Mark-to-Market Election

Once TTS-qualified, electing 475(f) on securities:

• Treats all open positions as sold at year-end at fair value.
• Converts gains and losses to ordinary income.
• Exempts you from the wash sale rule — critical for high-frequency equity strategies that constantly re-enter names.
• Removes the $3k annual capital loss limit; losses fully offset ordinary income.

Trap: electing 475(f) on commodities/futures converts Section 1256 60/40 treatment to ordinary income, usually worse. Elect on securities only.

Deadline: for individuals, attach the 475 election statement to the prior year's tax return or extension by April 15. So for tax year 2026 elections, the statement was due April 15, 2026. After making the election, file Form 3115 with that year's return.

(Green Trader Tax: Section 475 MTM, IRS Topic 429)

#Section 1256 (Futures, Broad-Based Index Options)

• 60% LTCG / 40% STCG regardless of holding period.
• Marked to market at year-end automatically.
• Reported on Form 6781.
• No wash sale issue.
• Top blended federal rate ~26.8%.

#LLC vs Sole Proprietor

A single-member LLC defaults to disregarded entity (tax-identical to sole prop), so the gain from an LLC is procedural, not tax-magic:

• Liability shield (limited — for trading, mostly cosmetic).
• Easier to add a passive S-corp management entity later to pay yourself a salary (enabling solo 401(k), HSA, employee benefits).
• Cleaner accounting separation.

At ~$50k+ profits, structure a trading LLC + S-corp management company combo. The S-corp pays you a reasonable salary (enables ~$70k/yr solo 401k contribution, HSA, etc.) and the trading LLC retains the rest as pass-through ordinary. This is the Green Trader Tax "Pro Trader Entity" structure.

#Other

• State tax: trading from TX, FL, WA, NV, TN, NH, SD, WY avoids state income tax. Trading from NY/CA/IL hits hard.
• Qualified Opportunity Zones: defer realized cap gains by reinvesting within 180 days. Useful if you have a large equity windfall.
• Wash sale aggregation: brokers report wash sales per-account, not cross-account. Same security across IBKR + Alpaca won't auto-aggregate; reconcile yourself or via tax software like TraderFyles or TradeLog.

#12. Defensive Diversification

Beyond the algo book, hold a tail in things that don't depend on any single counterparty, market, or political consensus:

Principle: things that survive any one institution failing. Mercury Treasury yields well but is ultimately a sweep into MMFs at partner banks. A small physical-asset tail (~5-10% of net worth) is cheap insurance against the rest of the structure failing for non-market reasons.

TreasuryDirect doesn't have a true public API, but its data feeds and ACH automation let you ladder T-Bills via recurring purchases programmed against your Mercury account.

#13. First 90 Days

#Phase 1 (Weeks 1-2): Substrate

• [ ] Open accounts: confirm public.com is funded; open Alpaca paper + Coinbase Advanced; open IBKR for futures access (takes ~1 week).
• [ ] Mercury API: generate token, build small Python script to pull daily balance to a sqlite/Postgres on STARGATE.
• [ ] Set up ~/dev/trading/ repo. Git. Direnv. uv or poetry for Python isolation.
• [ ] Install on STARGATE: Postgres + TimescaleDB, Grafana, Prometheus, ntfy.
• [ ] Subscribe to one paid data source initially: Tiingo ($30/mo) for daily equities, or Polygon Stocks Starter ($29/mo) for intraday.

#Phase 2 (Weeks 3-4): Research Environment

• [ ] Pull and warehouse 10 years of daily bars for SPY, QQQ, IWM, plus 100 large-cap names. Store as Parquet on STARGATE.
• [ ] Notebooks on moirai connecting to STARGATE Postgres.
• [ ] Implement and verify three classics from Chan's books on this data: pairs-trading on KO/PEP, momentum crossover on SPY, mean-reversion on AAPL. Confirm your backtest matches the book's results within ~10%.
• [ ] Pick framework: install both VectorBT and NautilusTrader; replicate one strategy in each.

#Phase 3 (Weeks 5-8): First Live Strategy

• [ ] Pick a single, simple, slow strategy. Recommended: time-series momentum on a basket of liquid futures (MES, MNQ, MCL, MGC, MZN), monthly rebalance, vol-targeted sizing. Or: cross-sectional momentum on top-200 US equities, monthly rebalance.
• [ ] Walk-forward backtest. Out-of-sample test. Deflated Sharpe Ratio.
• [ ] Deploy to Alpaca paper account for 2-4 weeks. Watch fills.
• [ ] Build a Grafana dashboard with: live equity curve, drawdown, per-position PnL, alerts on >2% daily drawdown.

#Phase 4 (Weeks 9-12): Real Capital, Real Risk

• [ ] Deploy real capital (start with 20-30% of intended live size) into the validated strategy on IBKR (futures) or Public/Alpaca (equities).
• [ ] Run side-by-side with paper for a month. Reconcile fills vs. expected. Quantify slippage.
• [ ] Add second uncorrelated strategy.
• [ ] Decide on TTS election for following tax year. If yes, draft 475(f) election statement.
• [ ] Begin reading Lopez de Prado in earnest — by now you've felt enough real-world pain to appreciate what he's solving.

#Forever (after Day 90)

• One new strategy in dev at a time.
• Monthly review: rolling Sharpe per strategy, allocation rebalance, archive any strategy with rolling 6-month Sharpe < 0.
• Quarterly review: framework upgrades, data source expansion, infra audit.
• Annual: tax planning with CPA, entity structure review.

#Appendix A: Quick Math Reference

• Sharpe: (R_p - R_f) / σ_p; annualize multi-period by √(periods_per_year).
• Sortino: same but only downside σ.
• Calmar: annualized return / max drawdown.
• Kelly: f* = p/a - q/b where p = win prob, q = 1-p, b = win size, a = loss size. Use ≤ half.
• OU half-life: λ = -ln(2) / β where β is the AR(1) coefficient minus 1.
• Hurst exponent: H < 0.5 mean-reverting, H > 0.5 trending. Compute on log-returns via R/S analysis or DFA.
• Deflated Sharpe Ratio: penalty term √((1-γ)Φ⁻¹(1-1/N) + γΦ⁻¹(1-1/(Ne))) where N is number of trials, γ Euler-Mascheroni.

#Appendix B: Minimum Viable Tech Stack

Hardware:    moirai (research) + STARGATE (execution)
OS:          Arch Linux (both)
Languages:   Python 3.12 (research/exec), Rust (perf-critical components later)
Frameworks:  VectorBT (sweep) + NautilusTrader (execute)
Storage:     TimescaleDB (live PnL, fills) + Parquet on disk (research data)
Broker:      public.com (equities/options/bonds), IBKR (futures), Coinbase Advanced (crypto)
Banking:     Mercury (operating + treasury sweep)
Monitoring:  Grafana + Prometheus + Loki on STARGATE
Alerts:      ntfy + ~/.local/bin/email
Supervision: systemd units per strategy
Tax:         TraderFyles or TradeLog for wash-sale reconciliation; Green Trader Tax CPA
Backups:     git for code; restic snapshots of TimescaleDB to Hetzner S3