Datasets:
protocol_id stringlengths 4 4 | stage stringlengths 11 25 | objective stringlengths 53 95 | fixed_variables listlengths 2 5 | changed_variable stringlengths 4 52 | promotion_gate stringlengths 56 96 | stop_rule stringlengths 31 89 | leakage_check stringlengths 55 96 | provenance stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|
P001 | Hypothesis registration | Turn an intuition into a falsifiable statement before candidate generation. | [
"economic mechanism",
"expected direction",
"target horizon",
"failure condition"
] | none | The hypothesis names a causal channel and an observation that would disprove it. | Reject vague stories that can explain every possible outcome. | Confirm that all proposed inputs are observable at the decision timestamp. | original |
P002 | Source audit | Separate authoritative definitions from automated or approximate interpretations. | [
"source version",
"publication date",
"market context"
] | interpretation candidate | Every material concept has traceable provenance and uncertainty labels. | Stop when required definitions or redistribution rights cannot be established. | Verify that quoted or derived content is licensed for the intended use. | original |
P003 | Portability audit | Decide whether the economic mechanism survives a change of market. | [
"causal channel",
"participant behavior",
"measurement meaning"
] | target-market institution | All required institutional conditions exist or have an explicit target analogue. | Reject syntax-only portability. | Use only target-market data known at the decision time. | original |
P004 | Candidate trio | Separate source fidelity from target-market adaptation. | [
"universe",
"evaluation window",
"risk settings"
] | faithful, adapted, or motif-hybrid construction | Each variant has distinct provenance and changes only the declared construction layer. | Reject comparisons that change data, settings, and structure simultaneously. | Freeze the candidate definitions before reading evaluation results. | original |
P005 | Fixed-settings comparison | Attribute outcome differences to one research choice. | [
"universe",
"delay",
"decay",
"neutralization",
"truncation"
] | one candidate component | The comparison is reproducible and paired under identical settings. | Discard confounded comparisons rather than explaining them after the fact. | Settings are chosen from a predeclared policy, not optimized on the outcome. | original |
P006 | Sign canonicalization | Distinguish a useful inverse relationship from a direction mistake. | [
"candidate structure",
"data",
"settings"
] | direction only | The inverse direction is economically interpretable and pre-registered as a separate hypothesis. | Do not search repeated sign and parameter combinations after observing outcomes. | Record the original sign and the rule that allowed one inversion. | original |
P007 | Small pilot | Buy enough evidence to retire weak mechanisms cheaply. | [
"pilot budget",
"promotion criteria",
"candidate roster"
] | pre-registered candidate variants | At least one variant clears the predefined signal-quality gate and beats its baseline. | End the mechanism when the fixed pilot budget produces no qualifying evidence. | Do not add candidates after seeing partial pilot results. | original |
P008 | Sequential stopping | Prevent exploration budgets from becoming permanent taxes. | [
"maximum budget",
"minimum effect",
"decision checkpoints"
] | sample size at scheduled checkpoints | Evidence exceeds the predeclared continuation threshold. | Stop at the registered deadline; do not move the baseline after failure. | Document every exception and treat a redesigned experiment as a new version. | original |
P009 | Signal-quality promotion | Promote only candidates with sufficient native predictive quality. | [
"quality thresholds",
"evaluation universe",
"cost assumptions"
] | candidate | The candidate clears all native quality requirements without repair. | Do not optimize downstream uniqueness for candidates that lack native quality. | Thresholds are defined before evaluating the candidate pool. | original |
P010 | Downstream gate diagnosis | Name the exact layer blocking an otherwise strong candidate. | [
"qualified candidate",
"native settings"
] | one downstream constraint | A paired intervention fixes the named gate without breaking prior gates. | Stop broad repair when paired canaries do not change the blocking gate. | Do not reuse final evaluation feedback as a generator training target without isolation. | original |
P011 | Robustness review | Check whether the mechanism survives reasonable changes without parameter mining. | [
"mechanism",
"direction",
"data provenance"
] | predeclared subperiod, universe, or nearby parameter | The effect remains directionally coherent across independent slices. | Reject isolated success that depends on one narrow slice or extreme parameter. | Hold out at least one slice that was not used in candidate selection. | original |
P012 | Public release review | Share methodology without releasing private signals, third-party content, or platform evidence. | [
"deny-set",
"license policy",
"public schema"
] | release artifact | Automated leakage scans pass and every row has original provenance. | Refuse publication on any exact overlap, secret, restricted metric, or uncertain license. | Compare all text against private expression stores and inspect the final manifest before upload. | original |
Portable Alpha Research Methods
An original educational dataset for research agents and human researchers who need to turn quantitative intuitions into falsifiable, portable experiments. It focuses on economic mechanisms, semantic translation, and disciplined experiment design rather than distributing trading signals.
What is included
- mechanisms — 24 records: hypothesis cards with economic intuition, portability conditions, failure modes, feature classes, and falsification tests.
- translation_rules — 20 records: exact, approximate, rejected, and policy-level rules for preserving meaning across data systems.
- experiment_protocols — 12 records: staged protocols for preregistration, paired tests, stopping rules, robustness, and safe publication.
from datasets import load_dataset
mechanisms = load_dataset("vuongtsc/alpha-research-methods", "mechanisms", split="train")
rules = load_dataset("vuongtsc/alpha-research-methods", "translation_rules", split="train")
protocols = load_dataset("vuongtsc/alpha-research-methods", "experiment_protocols", split="train")
Safety boundary
This repository contains no executable alpha expressions, private candidates, platform-derived backtests, portfolio outcomes, identifiers, credentials, data catalogs, or copied third-party formula collections. Every data row is an original synthesis released under CC BY 4.0.
The build compares every public text value against a private expression deny-set,
rejects formula-like syntax and secrets, enforces original provenance, and
uploads only a fixed file allowlist. See manifest.json for the build outcome.
Intended use
- training research agents to write falsifiable hypothesis cards;
- classifying faithful, approximate, and invalid research translations;
- designing paired experiments and preregistered stopping rules;
- teaching cross-market portability and leakage-aware research practice.
Limitations
- The records are methodological guidance, not empirical claims.
- Feature classes are intentionally generic and are not a data catalog.
- No record should be interpreted as investment advice or evidence of profitability.
- Users must validate market, data, legal, and operational assumptions independently.
License
CC BY 4.0. Attribution: vuongtsc, Portable Alpha Research Methods.
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