Algorithmic Trading Resources: The Due-Diligence Library
Algorithmic trading resources are usually a reading list. This is a library built around a harder question: for each source, what does it let you verify about a live operation?
Most of what builds real judgment is already public. A broker’s documentation admitting that duplicate order messages are normal. A regulator’s plain account of how messy live markets are. A venue’s written emergency plan. What is missing is the map. Nobody tells the person judging a strategy which document answers which question. In the decks we read, that gap shows fast: a manager explains the signal well, then goes quiet on what happens when the broker’s fill feed drops a trade.
For anyone weighing a strategy, a manager, or a platform, that reframes the task. The question stops being what to read and becomes what you can verify once you have, and whether you will know a strong answer when you hear one.
So the library is organized by the five dimensions The Review scores against. Every source sits under the dimension it helps you test. The framework is published in full below, free to run yourself.
How this algorithmic trading resources library is organized
The library has five sections, one for each dimension The Review scores, and they run in order of importance: risk management first, research discipline last. Every entry carries three lines. What it is, in one plain sentence. The specific thing it lets you verify. And where to start reading, so you skip the 300 pages that will not help.
External sources and our own pages sit in the same section, flagged where they are ours. Nothing earns a place for being famous. A source stays only if it changes what you can ask, check, or score. A thin section is honest. Padding is not.
Risk management: will the operation survive a bad month?
Risk management asks the first question, because weak risk control fails in ways a strong signal cannot repair. Track-record length, how position size is set, whether the manager can name what would break the strategy. These sources help you press on that.
- AQR research library. An open body of work on how factors crowd, fade, and behave in a crisis, with sourced data. Verify: whether a claimed profit is a known factor already crowded, and how it held up in past stress. Start at the factor-timing and crowding papers.
- Grinold & Kahn, Active Portfolio Management. The standard text on the information ratio and the arithmetic of active return. Verify: whether the skill a manager claims is even plausible given how often they can be right. Start at the fundamental law chapter.
- [ours] Risk management in algorithmic trading. Our plain-language read on why risk control decides investability more than performance does. Verify: your own view of a manager’s risk answers before you score them.
Protection controls: what the system does when conditions turn
Protection controls are the tactical layer. A circuit breaker on a violent move. A spread filter when liquidity thins. A time stop on a position that has gone stale. The test is whether these rules live in the code and have fired in anger, rather than sitting in a slide.
- SEC Market Access Rule (Rule 15c3-5, 2010). The US rule that made pre-trade risk controls mandatory for anyone reaching a market. Verify: whether an operation has hard pre-trade limits and a documented kill path, or only good intentions. Start at the pre-trade financial and regulatory controls section.
- [ours] Operational risk and controls. How kill switches, limits, and failure handling should be written down and owned. Verify: whether a manager’s protection rules have a named owner and a trigger, or live only in someone’s head.
Execution quality: where paper profit dies
A clean backtest can be taken apart by slippage in thin sessions, by orders that fill badly during news, by a system that assumes fills happen cleanly when they routinely do not. This section is about the gap between the intended trade and the one that prints.
- Interactive Brokers TWS API documentation. The broker’s own account of how order status and fills behave in production, including duplicate messages and fills that arrive only on execution reports. Verify: whether a manager’s reconciliation story survives the broker’s documented behaviour. Start at the order-status and execution-details sections.
- FIX protocol and FIXatdl. The messaging standard that lets execution-algo settings travel between systems without ambiguity. Verify: whether execution config is portable and precise, or hand-wired per broker. Start at the FIXatdl standard page.
- Larry Harris, Trading and Exchanges. The reference on market microstructure: order types, and how liquidity forms. Verify: whether a claimed execution approach respects how fills happen in the real book. Start at the order-driven markets chapters.
- [ours] Slippage in algorithmic trading. What separates a realistic fill model from a hopeful one. Verify: whether a backtest’s slippage assumption would survive a live thin session.
- [ours] Execution algorithms. How TWAP, VWAP, and implementation-shortfall logic behave once real size hits the book. Verify: whether a manager’s execution fits the venue and size they trade, or reaches for a default.
Operations: the failures that are not strategy failures
Most blow-ups in systematic trading are deployment accidents. A code push that skipped testing. A dead data feed the system kept trading on. A developer who fell ill and no one else had the login. These sources set the bar for running a system, and two of them are new law.
- Cboe Europe Emergency Playbook (2024). A live venue’s written plan for redundancy, disaster-recovery testing, and incident communication. Verify: what mature operational documentation looks like, as a benchmark for a manager’s own. Start at the recovery-testing and communication sections.
- DORA (Regulation (EU) 2022/2554). The EU operational-resilience law, applied since 17 January 2025, covering ICT risk, resilience testing, incident reporting, and vendor oversight. Verify: whether an EU-facing operation runs to a current resilience standard. Start at the ICT risk-management and testing articles.
- FINRA 2026 Annual Regulatory Oversight Report. The US regulator’s yearly priorities, with a sharpened focus on outages at third-party vendors. Verify: whether an operation knows and manages its upstream dependencies. Start at the third-party risk section.
- MiFID II Article 17. The EU duty, in force since 3 January 2018, to run resilient, capacity-tested systems with continuity plans. Verify: the baseline system-resilience obligations a firm should already meet. Start at Article 17 and its technical standard, RTS 6.
- [ours] trading platform due diligence. The operating layer under every strategy. Verify: whether the platform beneath a manager was built for the first bad week, not the demo.
- [ours] Platform documentation and the client integration guide. How to read a trading system’s architecture and its broker-connection layer. Verify: whether the wiring under a strategy is documented and repeatable, or improvised the week before.
Research discipline, and the market it runs in
Research discipline asks how the strategy got here and whether the developer stayed honest about what the tests showed. A graveyard of discarded ideas tells you more than a clean backtest. This section also holds the market-structure reading every other dimension quietly leans on.
- SEC 2020 Report on Algorithmic Trading in U.S. Capital Markets. The most detailed official account of how fragmented, data-heavy markets work. Verify: the market-structure assumptions a strategy is quietly built on. Start at the market-structure and order-routing sections.
- CFA Institute, Investment Model Validation (2024). A practitioner guide that turns model governance into concrete tests. Verify: whether a manager challenges, updates, and retires models, or simply runs them until they break. Start at the validation-process chapters.
- CFA Institute, AI Washing (June 2025). A report on telling real machine learning from ordinary statistics with a new label. Verify: whether an AI claim survives a plain set of questions. Start at the signs-and-symptoms section.
- Marcos López de Prado, Advances in Financial Machine Learning. The reference on why backtests overstate, with the deflated Sharpe ratio and proper cross-validation. Verify: whether a backtest survived honest validation or was tuned until it looked good. Start at the backtest-overfitting chapters.
- [ours] Backtesting hub. How to read a backtest for what it hides. Verify: whether an out-of-sample period was genuinely untouched, and how far live returns track the test.
- [ours] Strategy code. Reference implementations worth reading before you trust a system’s logic. Verify: whether the code does what the pitch says, on the paths nobody demos.
What each kind of resource can prove, and what it cannot
The mistake that wastes the most time is treating every source as equal evidence. A regulator’s rule, a vendor’s manual, and an academic paper answer different questions, and none of them answers the one that matters most: is this operation any good? The table sorts them by evidence weight.
| Resource type | What it can prove | What it cannot |
|---|---|---|
| Regulator report or rule | The standard an operation is meant to meet | Whether this operation meets it |
| Broker or vendor documentation | How the plumbing behaves: limits, callbacks, failure modes | Whether the manager built around it |
| Academic paper | Whether an approach holds up in general | Whether this implementation is sound |
| Backtest or code library | How a result was produced | Whether it survives live fills |
| Practitioner book | The principles and vocabulary to ask better questions | Anything specific to one live operation |
This is why the library is organized by verification rather than by format. A source is only as useful as the question you bring to it.
The methodology behind The Review, in the open and free to use
Most research shops treat their method as a badge. We publish ours as a tool. Score a manager with it yourself. Ask for the evidence each criterion names. The first covered strategy, the Quantitative Gold (XAUUSD) systematic-execution listing, shows the framework run end to end, and The Review holds the growing set.
The Review is editorial and evidence-based: due-diligence responses, live fill data, deployment logs, and a direct technical walkthrough with the developer. It is not a profile a manager writes about themselves. Coverage is selective, and most enquiries do not become a full review. That selectivity is the product.
Five dimensions, twenty-five criteria, no overall score
Every strategy is scored on the same five dimensions, five criteria each, on a 1-to-5 scale shown as bars on the listing. The dimensions run in order of importance. There is no blended score and no fixed weighting, on purpose: an investor who puts risk first will read the five bars differently from one who starts with research discipline. The framework’s job is to make the evidence legible and leave the judgment to the reader.
| Dimension | The five things scored (1–5 each) |
|---|---|
| 01 Risk management | Live track record; multi-environment evidence; position-sizing method; stress-event handling; failure-mode awareness |
| 02 Protection controls | Slippage controls; volatility-spike response; liquidity safeguards; stale-position controls; circuit breaker |
| 03 Execution quality | Backtest slippage realism; partial and no-fill handling; rejected-order handling; trade reconciliation; execution-window discipline |
| 04 Operations | Stack control; deployment process; unattended monitoring; vendor failover; continuity if the operator is unavailable |
| 05 Research discipline | Research process; out-of-sample validation; live-versus-backtest tracking; parameter stability; change management |
A score reflects the evidence provided for that criterion, weighed against what we can check rather than taken at face value. A profile of all fours and fives reads as marketing and gets treated as such. The genuine gaps are left low, because a strategy that scores four on risk and one on operations is telling you something worth seeing.
The standards every page is written to
Primary sources. Every claim with a number, a date, or a named party ties to a source you can open: a regulatory document, a peer-reviewed paper, firm research we can point to, or an official filing. We do not cite aggregators as evidence.
Be specific. “Some managers” becomes “managers running short-horizon equity strategies.” Vague dates become real ones. Where the evidence lets us be precise, we are.
Take a position. Staying neutral between a strong operation and a shiny shallow one is its own kind of misinformation. Where the evidence supports a view, we state it. Where it is genuinely mixed, we say that instead.
Written by people. Every page is written and checked by people. We do not run text through a model and publish it as though a person wrote it.
Corrections. Pages carry a published date and an updated date. When facts change, or we get something wrong, we fix it in the open.
Conflict of interest policy
3 Quant Research takes no payment for coverage, scoring, or placement from any manager, platform, broker, or provider it covers. The policy is short.
No paid placement in The Review. You cannot buy your way in. You cannot buy a higher score. You cannot buy faster coverage.
No affiliate links. No affiliate links to brokers, platforms, or data vendors anywhere in systematic trading. Mentions are editorial. Links are for reading.
No favours. Having spoken with a manager or platform before, including a walkthrough, changes nothing in the scoring, and we disclose it where it matters.
No advertising. No display ads and no sponsored content on the site.
One paid relationship, disclosed. Where we introduce a reader to a manager and that introduction leads to an allocation, the manager may pay us a fee. It is disclosed on the listing, it is never a condition of coverage, and it cannot change a score.
The work is funded by the concierge conversation service for private investors, family offices, and investment committees, by the introducer fees described above, and in future by research subscriptions.
Questions we get asked about algorithmic trading resources and methodology
Meet the strategies that cleared all five dimensions
Everything on this page is the work we do so you don’t have to. We put real algo and quant strategies through all five dimensions and publish only the ones that hold up, each open to invest in. This is the payoff behind all the reading: strategies you can act on.