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An institution of few  ·  Est. 2024

Systematic trading research, built and scored in the open.

Monolith Research is a systematic-research practice — an institution of few. What reaches the record is held to a standard hard enough that most of it does not: systematic strategy studies, documented research infrastructure, and an open-source validation gate.

The firm open-sources the gate it judges itself by, documents retired strategies in public, and separates archived simulations from actual execution. Signal logic still in use is kept private; the method, the evidence, and the failures are made legible. It does not manage client capital or provide investment advice.1

ETFs  ·  FX  ·  Commodities United Kingdom
§Selected results
Read the full context ↗

Different systems.
Different outcomes.

Selected positive-return simulations, not live performance and not the complete research record. Ordered by source-reported Sharpe where available; other ratios are unranked. S17 and the deployed OU configuration are omitted from this view.

  1. Reported Sharpe #1Lane 46 · time-series mean reversionSharpe 1.653
  2. Reported Sharpe #2CWMR · paper replicationSharpe 1.550
  3. Not Sharpe-rankedOU Top 5Reported CAGR / bar vol: 1.72
  4. Not Sharpe-rankedGold H4 · v1 configurationReported CAGR / vol: 0.92
Source-reported JSON · selection-biased / partial

Lane 46 · time-series mean reversion

1 Jan 2026 — 21 Aug 2026
Source-model turnover costs deducted; not verified execution costs

Annualised return
Strategy+235.92%
S&P 500+19.61%
Strategy cumulative: +116.73% · 233 calendar daysGeometric extrapolation · not a forecast
Source-reported Sharpe
Strategy1.65
S&P 5001.39
Strategy: Source-reported Sharpe · archive estimatorS&P 500: daily Sharpe · √252 · risk-free rate 0
Drawdown / P&L decline
Strategy-38.02 pp
S&P 500-9.10%
Strategy: source cumulative-P&L decline in percentage points, not peak-equity drawdownS&P 500: daily closes, including the pre-window baseline · not intraday lows

Strategy vs S&P 500

Matching window · annualised returns
Lane 46 · time-series mean reversion+235.92%
S&P 500+19.61%

S&P 500 cumulative: +12.11% · Cumulative gap: +104.63 percentage points above the index.

USD price return · dividends excluded. Recomputed from Yahoo Finance / ^GSPC ↗.

Read this comparison carefully. This is a market reference, not risk-matched alpha: assets, leverage and costs differ. The index excludes dividends; a total-return benchmark would differ. Strategy inputs are source-reported, not independently reconstructed.

Monthly return · partial first / last months
2026-01+3.53%
2026-02+39.90%
2026-03+19.75%
2026-04-10.89%
2026-05+0.85%
2026-06+15.34%
2026-07+50.69%
2026-08-2.43%

Selection bias: the configuration and instrument subset were selected within the reported window; the headline includes the selection/training period, not a fully out-of-sample track record. Source coverage is partial (M30 inputs missing). A separate de-selected configuration reports OOS Sharpe 0.28; that is not this headline configuration. The source uses position-sign flips as activity counts, not independent completed trades.

Calculation & evidence Formulas, sources & conventions

Strategy evidence

Configuration:Lane 46 · time-series mean reversion

Annualised return on this website is calculated geometrically from the source-reported cumulative return and stated calendar window; it is not the archive’s separately reported annual-return field. Sharpe is source-reported, not independently recomputed. Do not rank it against differently measured ratios.

Source:46_zoo_ts_meanrev.json · Archived simulation — not live performance. Source files are retained privately; strategy parameters and execution logic are not published here.

Benchmark & annualisation

Daily observed trading-close returns: mean / sample standard deviation × √252; risk-free rate 0. USD price index, dividends excluded. Drawdown uses closing prices including the pre-window baseline, not intraday lows.

160 observed daily returns. Maximum drawdown peak: 27 Jan 2026; trough: 30 Mar 2026.

Baseline: 2025-12-31 close (6845.50); endpoint: 2026-08-21 close (7674.37). The last close before the window covers the opening holiday. Annualisation uses the same 233-day window as the strategy.

Annualised return = [(1 + cumulative return / 100)^(365 / days) − 1] × 100. Days include both stated dates, using UTC calendar days. Sub-year rates extrapolate the observed period; they are not earned one-year returns. Source-reported inputs remain unvalidated after annualisation. Monthly bars remain actual period returns, not annualised.

Source-reported JSON · unvalidated

CWMR · paper replication

1 Jan 2026 — 21 Aug 2026
Costs excluded

Annualised return
Strategy+10.87%
S&P 500+19.61%
Strategy cumulative: +6.81% · 233 calendar daysGeometric extrapolation · not a forecast
Source-reported Sharpe
Strategy1.55
S&P 5001.39
Strategy: Source-reported Sharpe · archive estimatorS&P 500: daily Sharpe · √252 · risk-free rate 0
Maximum drawdown
Strategy-3.14%
S&P 500-9.10%
Strategy: based on saved observationsS&P 500: daily closes, including the pre-window baseline · not intraday lows

Strategy vs S&P 500

Matching window · annualised returns
CWMR · paper replication+10.87%
S&P 500+19.61%

S&P 500 cumulative: +12.11% · Cumulative gap: -5.30 percentage points below the index.

USD price return · dividends excluded. Recomputed from Yahoo Finance / ^GSPC ↗.

Read this comparison carefully. This is a market reference, not risk-matched alpha: assets, leverage and costs differ. The index excludes dividends; a total-return benchmark would differ. Strategy inputs are source-reported, not independently reconstructed.

Monthly return · partial first / last months
2026-01+3.05%
2026-02-2.26%
2026-03+4.24%
2026-04+2.09%
2026-05+0.85%
2026-06-2.73%
2026-07+1.20%
2026-08+0.37%

Separate configuration / paper proxy. No standalone raw returns ledger available for this archived summary.

Calculation & evidence Formulas, sources & conventions

Strategy evidence

Configuration:CWMR · paper replication

Source-reported ratio; not independently recomputed from raw returns. The archive estimator has not been harmonised with the daily Sharpe computed above. Do not use these different conventions as a ranking.

Source:09_cwmr_2011.json · Archived simulation — not live performance. Source files are retained privately; strategy parameters and execution logic are not published here.

Benchmark & annualisation

Daily observed trading-close returns: mean / sample standard deviation × √252; risk-free rate 0. USD price index, dividends excluded. Drawdown uses closing prices including the pre-window baseline, not intraday lows.

160 observed daily returns. Maximum drawdown peak: 27 Jan 2026; trough: 30 Mar 2026.

Baseline: 2025-12-31 close (6845.50); endpoint: 2026-08-21 close (7674.37). The last close before the window covers the opening holiday. Annualisation uses the same 233-day window as the strategy.

Annualised return = [(1 + cumulative return / 100)^(365 / days) − 1] × 100. Days include both stated dates, using UTC calendar days. Sub-year rates extrapolate the observed period; they are not earned one-year returns. Source-reported inputs remain unvalidated after annualisation. Monthly bars remain actual period returns, not annualised.

Source-reported JSON · unvalidated

OU Top 5

1 Jan 2026 — 21 Aug 2026
Costs excluded

Annualised return
Strategy+39.15%
S&P 500+19.61%
Strategy cumulative: +23.48% · 233 calendar daysGeometric extrapolation · not a forecast
Return / risk ratios
Strategy1.72
S&P 5001.39
Strategy: Reported CAGR / bar vol · NOT conventional SharpeS&P 500: daily Sharpe · √252 · risk-free rate 0
Maximum drawdown
Strategy-25.74%
S&P 500-9.10%
Strategy: based on saved observationsS&P 500: daily closes, including the pre-window baseline · not intraday lows

Strategy vs S&P 500

Matching window · annualised returns
OU Top 5+39.15%
S&P 500+19.61%

S&P 500 cumulative: +12.11% · Cumulative gap: +11.37 percentage points above the index.

USD price return · dividends excluded. Recomputed from Yahoo Finance / ^GSPC ↗.

Read this comparison carefully. This is a market reference, not risk-matched alpha: assets, leverage and costs differ. The index excludes dividends; a total-return benchmark would differ. Strategy inputs are source-reported, not independently reconstructed.

Monthly return · partial first / last months
2026-01+7.55%
2026-02-1.51%
2026-03-13.71%
2026-04+10.43%
2026-05+6.15%
2026-06+18.67%
2026-07-10.48%
2026-08+8.49%

Separate configuration / paper proxy. No standalone raw returns ledger available for this archived summary.

Calculation & evidence Formulas, sources & conventions

Strategy evidence

Configuration:Pre-recalibration configuration; distinct from the deployed configuration.

CORRECTED: source run_lane05.py lines 75-79 divides bar-count-annualised compounded return by annualised bar volatility. Its reported 1.72 is NOT conventional Sharpe. The window is a pre-recalibration configuration, not the deployed configuration.

Source:05_optimised_top5_prerecal.json · Archived simulation — not live performance. Source files are retained privately; strategy parameters and execution logic are not published here.

Benchmark & annualisation

Daily observed trading-close returns: mean / sample standard deviation × √252; risk-free rate 0. USD price index, dividends excluded. Drawdown uses closing prices including the pre-window baseline, not intraday lows.

160 observed daily returns. Maximum drawdown peak: 27 Jan 2026; trough: 30 Mar 2026.

Baseline: 2025-12-31 close (6845.50); endpoint: 2026-08-21 close (7674.37). The last close before the window covers the opening holiday. Annualisation uses the same 233-day window as the strategy.

Annualised return = [(1 + cumulative return / 100)^(365 / days) − 1] × 100. Days include both stated dates, using UTC calendar days. Sub-year rates extrapolate the observed period; they are not earned one-year returns. Source-reported inputs remain unvalidated after annualisation. Monthly bars remain actual period returns, not annualised.

Source-reported JSON · unvalidated

Gold H4 · v1 configuration

1 Jan 2026 — 21 Aug 2026
Costs excluded

Annualised return
Strategy+29.86%
S&P 500+19.61%
Strategy cumulative: +18.15% · 233 calendar daysGeometric extrapolation · not a forecast
Return / risk ratios
Strategy0.92
S&P 5001.39
Strategy: Reported CAGR / vol · NOT conventional SharpeS&P 500: daily Sharpe · √252 · risk-free rate 0
Maximum drawdown
Strategy-16.74%
S&P 500-9.10%
Strategy: based on saved observationsS&P 500: daily closes, including the pre-window baseline · not intraday lows

Strategy vs S&P 500

Matching window · annualised returns
Gold H4 · v1 configuration+29.86%
S&P 500+19.61%

S&P 500 cumulative: +12.11% · Cumulative gap: +6.04 percentage points above the index.

USD price return · dividends excluded. Recomputed from Yahoo Finance / ^GSPC ↗.

Read this comparison carefully. This is a market reference, not risk-matched alpha: assets, leverage and costs differ. The index excludes dividends; a total-return benchmark would differ. Strategy inputs are source-reported, not independently reconstructed.

Monthly return · partial first / last months
2026-01+14.17%
2026-02+6.05%
2026-030.00%
2026-04-1.64%
2026-05-0.79%
2026-060.00%
2026-070.00%
2026-080.00%

Only six recorded trades. Missing volume changes the leverage behaviour; fills approximate the signal-bar close.

Calculation & evidence Formulas, sources & conventions

Strategy evidence

Configuration:Gold H4 · v1 configuration

The archive calls this Sharpe, but its implementation divides annualised compounded return by daily volatility, not mean excess return by volatility. This is a CAGR/volatility ratio, NOT a conventional Sharpe ratio.

Source:02_gold_v1.json · Archived simulation — not live performance. Source files are retained privately; strategy parameters and execution logic are not published here.

Benchmark & annualisation

Daily observed trading-close returns: mean / sample standard deviation × √252; risk-free rate 0. USD price index, dividends excluded. Drawdown uses closing prices including the pre-window baseline, not intraday lows.

160 observed daily returns. Maximum drawdown peak: 27 Jan 2026; trough: 30 Mar 2026.

Baseline: 2025-12-31 close (6845.50); endpoint: 2026-08-21 close (7674.37). The last close before the window covers the opening holiday. Annualisation uses the same 233-day window as the strategy.

Annualised return = [(1 + cumulative return / 100)^(365 / days) − 1] × 100. Days include both stated dates, using UTC calendar days. Sub-year rates extrapolate the observed period; they are not earned one-year returns. Source-reported inputs remain unvalidated after annualisation. Monthly bars remain actual period returns, not annualised.

Selected results are not a combined portfolio or a complete track record. The ordering is by reported Sharpe values, not a harmonised performance ranking; costs, sample conventions and selection methods differ. OU and Gold use compounded-return/volatility ratios and are not Sharpe-ranked. Recomputing ledger arithmetic does not establish out-of-sample validity or an executable edge.

The practice

An idea is only
the beginning.

Inside Monolith

We connect market questions to working code. From the first hypothesis to the data, the test and the decision that follows.

Monolith Research is an independent research project founded by Bilal Malik. The work spans systematic strategies, alternative datasets and the software needed to examine both critically.

Research-ledBuilt in PythonEvidence-aware
01Selected work
A closer look

The work behind
the wordmark.

Not just a catalogue of ideas. Research tools, experimental systems and the reasoning that connects them.

Research → evidence
The burden of proof.
01 / SignalWhat is the mechanism?
02 / TestWhat could falsify it?
03 / DecisionWhat does the record support?
Research software / Open source

A framework for
asking harder questions.

Probabilistic and Deflated Sharpe, backtest-overfitting estimates and explicit evidence boundaries. A closer look at the statistical tools behind a research decision.

Explore the validation framework
02Research domains
Browse all research

Different markets.
One research discipline.

Price is the starting point, not the whole explanation. The research library examines relationships, constraints and the information available to a decision-maker at the time.

03From question to system
Method over mythology

Think clearly.
Build carefully.
Keep the evidence.

The implementation matters as much as the hypothesis. Timing, costs and data quality can change the answer before a model ever gets involved.

Inside the research stack
  1. Start with a mechanism

    Ask why a price relationship should exist, who creates it and what would make it disappear.

  2. Build the experiment

    Make data availability, signal construction and execution assumptions explicit.

  3. Challenge the result

    Separate model fit from evidence. Examine costs, selection, leakage and alternative explanations.

  4. Leave a usable record

    Preserve the implementation, limitations and decision so the next question starts further ahead.

§ 9   The Founder An institution of few. One discipline. The Team · UK

Bilal Malik

Founder · Monolith Research

London-based, reading Finance at Lancaster University. He founded Monolith Research to study liquid markets — ETFs, FX, and commodities — the way a journal studies a result: write the hypothesis down, test it against the record, and let the statistics, not the narrative, decide.

He set the method and built the core research stack end to end; the firm is led from the front and carried by a small, growing team. What survives that standard is published with the evidence behind it — the kind a careful reader can audit a year later, not the kind that lasts only while the screenshots are fresh.

The team carries institutional market exposure through Susquehanna International Group and Schroders — quantitative trading, market-making, and multi-asset portfolio construction on both sides of the market.

Meet the team
Research correspondence

Good questions
deserve a conversation.

The interesting work often begins between disciplines: a market mechanism, a statistical question and a difficult implementation.

Explore selected technical work and the decisions behind it, or get in touch to discuss a research question.