A researcher that shows its sources.
The agent plans experiments and reads the results, and every figure it quotes links back to a deterministic engine run.
Why does the ORB lose money on Fridays?
Friday is the only weekday with negative expectancy, per trade over trades, against overall. The losses sit in entries late in the morning. I have not measured how many reverse through the range: no source for fri.reversal_share.
Run a study of late Friday entries so the reversal share has a source
Illustrative session. Figures are examples, not performance claims.
The agent researches. The engine decides.
Anything shown as a result comes from a deterministic engine job. The agent's writing is stored beside it as interpretation.
Every claim has a type and a source.
The type decides what a finding must cite, and numbers inside it are references, not text.
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- ConclusionTime of dayfnd_01J9ORB038
The Friday weakness sits late in the morning and appears in both halves of the development range.
Citessupportsrun_01J9ORBDEMO07supportsrun_01J9ORBDEMO09
- EvidenceExperiment resultfnd_01J9ORB035
Skipping Friday entries after 11:00 moves expectancy from to .
Citessupportsrun_01J9ORBDEMO07interpretsrun_01J9ORBDEMO01
- HypothesisParticipationfnd_01J9ORB032
Late-morning Friday breakouts fail because participation thins before the weekend.
Tested byexp_01J9ORB019supersedesfnd_01J9ORB029
- Hypothesisfnd_01J9ORB029Superseded
Friday losses come from lower volatility at the open.
- ObservationWeekday splitfnd_01J9ORB031
Friday entries have the lowest expectancy of any weekday: .
- Supports the thesis
- 9
- Contradicts it
- 5
- Findingfnd_01J9ORB027Open
Volume filter improves expectancy
contradictsrun_01J9ORBDEMO12Run on 2020 alone: the filter removes the strongest month
- Findingfnd_01J9ORB032Open
Friday participation thins after 11:00
contradictsrun_01J9ORBDEMO11Friday volume after 11:00 is within the range of other weekdays
- Findingfnd_01J9ORB018Superseded
Edge is stronger on volatile days
contradictsrun_01J9ORBDEMO05The top volatility decile has a negative mean and the widest interval
Is this still the strategy you described?
Partly. A challenger tested against the thesis on the same data.
initialThe strategy exactly as you described it.
- Preserved
- 30-minute rangebreakout entrystop across range2R targetvolume condition
implementation_fixThe range now closes at 10:00 ET exactly.
- Preserved
- opening rangebreakout entrystop across range2R targetvolume condition
- Changed
- range end bar
consistent_refinementAbove-average volume now means 20-day relative volume.
- Preserved
- opening rangebreakout entrystop across range2R target
- Changed
- volume measure
challengerAdds a VWAP-distance filter the thesis never mentioned.
- Preserved
- opening rangebreakout entrystop across range2R target
- Added
- VWAP-distance filter
departureFades the breakout after 11:00 ET instead of following it.
- Preserved
- opening rangestop across range
- Changed
- entry direction after 11:00
- Added
- time-of-day switch
- Removed
- breakout-only entry
Bad news gets the same billing.
Contradicting results stay open in the evidence graph, and every new version is checked against your original thesis.
- Contradictions stay open until someone deals with them
- Evidence for and against is shown at equal weight
- Each version is classified from fix to departure
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A campaign runs the research loop without you, inside limits on experiments, compute, agent credits and what the agent may see.
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Is the Friday effect real?
Experiment 1
Budget used
Permitted methods
- Backtest
- Optimisation
- Monte Carlo
- Cost stress
- ML comparison
Structural changes, extra scope and validation are outside this authorisation.
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- whoamireadmember · workspace research · 6 scopes
- get_strategy_workflowreadbaseline accepted · implementation review next
- submit_strategy_backtestcomputequeued · job_01J9ORB07
{ "versionId": "28d795ee-…", "timeframe": "PT5M", "rangeStartUtc": "2021-12-06", "baseline": false } - wait_for_jobreadaccepted · run_01J9ORB07
- get_run_resultsreadaccepted metrics
{ "runId": "run_01J9ORB07", "resultHash": "sha256:9f2c41e7…", "metrics": { "expectancyR": 0.14, "trades": 1284, … }, "artifacts": ["trades:7c41d8", "equity:7c41d8"] } - request_mandate_authorizationmandaterefused · INVALID_ARGUMENTS
Every mandate range must be development history. Validation and sealed history is reserved.
- request_human_decisionresearchmandate_authorization · awaiting you
Recorded in Reviews → Agent requests: authorize mandate
Results come back as artefact ids and hashes, never pasted data.
Illustrative session; tool names and scopes are from catalogue 2026-10-21.5. ML training and studies run inside a mandate you authorise.
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