Open data, free forever

Every number here
is an endpoint
you can call.

TradeClaw gives the data away. The cost-adjusted track record, the per-trade cost field, and the full history that feed every chart on this site are live JSON APIs, and the research that killed each strategy is committed to the repo as raw backtest JSON. Nothing is hardcoded, nothing is behind a tier. Call it, cache it, fork it, reproduce it.

The full reference lives at /api-docs. Everything below runs against https://tradeclaw.win.

Live endpoints

Read-only GET routes. Responses carry public cache headers (s-maxage of 60 seconds on the signal and cost-field routes) so a reverse proxy or your own cache can absorb repeat calls.

GET/api/research/cost-fieldnew

One entry per resolved sized trade as parallel arrays: gross R before cost, real recorded round-trip cost in R, and asset class. Net R per trade is grossR[i] minus costR[i]. This is the raw dataset behind the homepage Cost Field scene, with no aggregation applied.

scope = pro | broadcast: full track record (default) or gate-approved rows only
period: optional window filter, same grammar as the history route

Response shape

{ classes: ["crypto","metals","fx"], count, resolvedTotal, t[], grossR[], costR[], cls[] }

curl https://tradeclaw.win/api/research/cost-field

GET/api/signals/equity

Cost-adjusted equity curve plus a summary block. Each sized trade risks 1% of equity and is charged its real recorded execution cost, so the curve and the net expectancy figure are what a subscriber would actually have realized.

summaryOnly = 1 | true: drop the point array, return summary and rolling win rates only
scope = pro | broadcast: full record (default) or gate-approved rows
band = premium | standard | all: filter by confidence band
category: asset-category filter (e.g. crypto, fx)
period: optional window filter
smooth = median2x | median3x: opt-in R-cap on the curve; off by default

Response shape

{ points[], summary: { totalReturn, maxDrawdown, winRate, sizedTrades, expectancyR, netExpectancyR, avgCostR, roundTripCostPct, breakEvenWinRate, sharpeRatio, hardRCap, ... }, rollingWinRates: { "7d","30d","90d" }, band, scope, category, smooth }

curl 'https://tradeclaw.win/api/signals/equity?summaryOnly=1&scope=pro'

GET/api/signals/history

Resolved signal history, paginated, with the same win-rate and resolved counts every other surface uses. Add format=csv to stream the full filtered set as a CSV file with an id, pair, direction, prices, and 4h/24h outcome columns.

format = json | csv: JSON page (default) or a downloadable CSV of the filtered set
pair: single symbol, e.g. BTCUSD
direction = BUY | SELL: filter by trade side
outcome = win | loss | pending: filter by resolved outcome
limit = max 200: page size, default 50
offset: pagination offset
scope / category / period / sort: record scope, asset category, time window, ordering (sort=resolved-first)

Response shape

{ records[], total, offset, limit, scope, category, earliestTimestamp, latestTimestamp, stats: { totalSignals, resolved, wins, losses, winRate, avgConfidence, streak, bestSignal, ... } }

curl 'https://tradeclaw.win/api/signals/history?limit=50&outcome=win'

CSV export (16 columns, opens in any spreadsheet)

curl 'https://tradeclaw.win/api/signals/history?format=csv' -o tradeclaw-history.csv

GET/api/calibration

The reliability data behind the /calibration page. Signals are bucketed into five confidence bands and each band reports its real win rate, so you can see whether a 70% call actually wins 70% of the time. Includes Brier score and expected calibration error over the resolved population. Cached 5 minutes (revalidate 300).

(none): no query params; measures the full counted-resolved population

Response shape

{ buckets: [5 confidence bands], overallAccuracy, totalSignals, brier, ece, calibration, windowStart, windowEnd, insufficientData, minStableSignals, updatedAt }

curl https://tradeclaw.win/api/calibration

Rendered on /calibration.

GET/feed.json

The 50 most recent signals as a subscribable feed. Also served as RSS 2.0 at /feed.xml and Atom 1.0 at /atom.xml for any reader.

(none): point a JSON Feed, RSS, or Atom reader at the URL

Response shape

JSON Feed 1.1: { version, title, feed_url, items: [ { id, url, title, content_html, summary, date_published, tags } ] }

curl https://tradeclaw.win/feed.json

Committed research artifacts

The 2026 signal archive is committed to data/ at the repo root, and every verdict on /research is backed by a deterministic backtest JSON in docs/research/experiments/. Each experiment file holds the full spec and results, so a given candle store state reproduces the same numbers byte for byte.

data/signal-log.jsontimestamped log

The historical git-committed signal log, frozen 2026-05-02 when in-repo logging was retired. Each record carries entry, tp1, sl, confidence, strategy, and 4h/24h outcomes, and for that window git history is the look-ahead proof: each commit timestamps a signal before its outcome was known. Newer runs are recorded in the signal_run_log database table with a SHA-256 audit trail.

data/SIGNAL-LOG.mdhuman-readable

The same historical log in a readable table, covering the same frozen window. Verify provenance yourself with git log --oneline data/signal-log.json.

REGISTRY.mdrun ledger

Append-only ledger of every backtest. One line per run with its spec and headline result. Read it before re-testing a hypothesis; if the spec ran before, the answer is here.

BTCUSD-H1-2024-06-10-2026-06-09-live-crypto-classic_regime-aware_hmm-top3_vwap-ema-bb_full-risk-f4.json9.5 KB

BTCUSD H1, five entry strategies scored under real crypto costs. Best run (regime-aware) 0.6% at 50% win rate; classic loses 11.1%.

BTCUSD-H1-2024-06-10-2026-06-09-legacy-zero-classic_regime-aware_hmm-top3_vwap-ema-bb_full-risk-f4.json9.4 KB

Same BTCUSD H1 window with zero costs and legacy fixed 2:1 sizing. The cost-free counterfactual to the live-crypto run above.

carry-validation-10majors-f4.json300 KB

Carry strategy across 10 majors with two-leg costs. All three variants (always-on, threshold, top-3 rotation) fail the deployment gate.

daily-momentum-validation-BTCUSD_ETHUSD_SOLUSD_BNBUSD_XRPUSD_ADAUSD_DOGEUSD_DOTUSD_LINKUSD_AVAXUSD-D1-f4.json141 KB

Daily time-series momentum across 10 crypto majors, D1. No config survives ~0.4% crypto costs robustly; the two positive symbols are flukes.

daily-momentum-validation-BTCUSD-D1-f4.json20 KB

Daily momentum isolated to BTCUSD, D1. Single-symbol slice of the majors validation for a focused read.

regime-hmm-walkforward-BTCUSD_ETHUSD_SOLUSD-H1-2024-06-12-2026-06-11.json13 KB

Three-state structural regime HMM (trend, volatile, range) on BTC/ETH/SOL H1, walk-forward. States price magnitude, not drift; no directional premium.

regime-routed-walkforward-BTCUSD_ETHUSD_SOLUSD-H1-2024-06-01-2026-06-01-f4.json52 KB

Per-regime routed entries on BTC/ETH/SOL H1 walk-forward. The only non-thin routed cell is negative on all three symbols; the gate fails on paper.

xsection-validation-30majors-D1-lb14-rb7-top5-f4.json9.3 KB

Cross-sectional momentum, 30 majors D1, top-5. Long-only and long-short both fail against the basket benchmark over the full window.

Use it

Three recipes against the live routes. Each references only fields that actually appear in the responses above.

1. Shell: count net-positive trades

# Count net-positive trades in the cost field (net R = grossR - costR)
curl -s https://tradeclaw.win/api/research/cost-field \
  | jq '[.grossR as $g | .costR as $c
         | range(0; .count)
         | select($g[.] - $c[.] > 0)] | length'

2. JavaScript: read the net edge per trade

// Load the cost-adjusted equity summary and print the net edge per trade
const res = await fetch(
  'https://tradeclaw.win/api/signals/equity?summaryOnly=1&scope=pro'
);
const { summary } = await res.json();

console.log('net expectancy  R/trade:', summary.netExpectancyR);
console.log('gross expectancy R/trade:', summary.expectancyR);
console.log('avg round-trip cost R:', summary.avgCostR);

3. Python: mean net R by asset class

# Load the cost field into a DataFrame and compute mean net R
import requests, pandas as pd

d = requests.get('https://tradeclaw.win/api/research/cost-field').json()
df = pd.DataFrame({
    't': d['t'],
    'grossR': d['grossR'],
    'costR': d['costR'],
    'cls': d['cls'],
})
df['netR'] = df['grossR'] - df['costR']
df['asset'] = df['cls'].map(dict(enumerate(d['classes'])))

print('mean net R/trade:', df['netR'].mean())
print(df.groupby('asset')['netR'].mean())

License and terms

The code is MIT licensed. Use, modify, and redistribute it freely, including the API routes and the research pipeline. The datasets carry the same spirit: take them, cite them, build on them. Source is at github.com/naimkatiman/tradeclaw.

There is no API key and no published rate limit on these read-only routes. As a courtesy, cache responses on your side rather than polling in a tight loop; the s-maxage headers make that cheap.

Informational only, not advice.

This data is the recorded output of a research engine, published for transparency. It is not financial advice and not a profit claim. The headline finding is that after real execution costs the engine has no net edge. Treat every number as evidence to audit, not a recommendation to trade.

Every number here
is an endpoint
you can call.

The full reference lives at /api-docs. Everything below runs against https://tradeclaw.win.

Live endpoints

Read-only GET routes. Responses carry public cache headers (s-maxage of 60 seconds on the signal and cost-field routes) so a reverse proxy or your own cache can absorb repeat calls.

GET/api/research/cost-fieldnew

scope = pro | broadcast: full track record (default) or gate-approved rows only
period: optional window filter, same grammar as the history route

Response shape

{ classes: ["crypto","metals","fx"], count, resolvedTotal, t[], grossR[], costR[], cls[] }

curl https://tradeclaw.win/api/research/cost-field

GET/api/signals/equity

summaryOnly = 1 | true: drop the point array, return summary and rolling win rates only
scope = pro | broadcast: full record (default) or gate-approved rows
band = premium | standard | all: filter by confidence band
category: asset-category filter (e.g. crypto, fx)
period: optional window filter
smooth = median2x | median3x: opt-in R-cap on the curve; off by default

Response shape

{ points[], summary: { totalReturn, maxDrawdown, winRate, sizedTrades, expectancyR, netExpectancyR, avgCostR, roundTripCostPct, breakEvenWinRate, sharpeRatio, hardRCap, ... }, rollingWinRates: { "7d","30d","90d" }, band, scope, category, smooth }

curl 'https://tradeclaw.win/api/signals/equity?summaryOnly=1&scope=pro'

GET/api/signals/history

format = json | csv: JSON page (default) or a downloadable CSV of the filtered set
pair: single symbol, e.g. BTCUSD
direction = BUY | SELL: filter by trade side
outcome = win | loss | pending: filter by resolved outcome
limit = max 200: page size, default 50
offset: pagination offset
scope / category / period / sort: record scope, asset category, time window, ordering (sort=resolved-first)

Response shape

{ records[], total, offset, limit, scope, category, earliestTimestamp, latestTimestamp, stats: { totalSignals, resolved, wins, losses, winRate, avgConfidence, streak, bestSignal, ... } }

curl 'https://tradeclaw.win/api/signals/history?limit=50&outcome=win'

CSV export (16 columns, opens in any spreadsheet)

curl 'https://tradeclaw.win/api/signals/history?format=csv' -o tradeclaw-history.csv

GET/api/calibration

(none): no query params; measures the full counted-resolved population

Response shape

{ buckets: [5 confidence bands], overallAccuracy, totalSignals, brier, ece, calibration, windowStart, windowEnd, insufficientData, minStableSignals, updatedAt }

curl https://tradeclaw.win/api/calibration

Rendered on /calibration.

GET/feed.json

The 50 most recent signals as a subscribable feed. Also served as RSS 2.0 at /feed.xml and Atom 1.0 at /atom.xml for any reader.

(none): point a JSON Feed, RSS, or Atom reader at the URL

Response shape

JSON Feed 1.1: { version, title, feed_url, items: [ { id, url, title, content_html, summary, date_published, tags } ] }

curl https://tradeclaw.win/feed.json

Committed research artifacts

data/signal-log.jsontimestamped log

data/SIGNAL-LOG.mdhuman-readable

The same historical log in a readable table, covering the same frozen window. Verify provenance yourself with git log --oneline data/signal-log.json.

REGISTRY.mdrun ledger

Append-only ledger of every backtest. One line per run with its spec and headline result. Read it before re-testing a hypothesis; if the spec ran before, the answer is here.

BTCUSD-H1-2024-06-10-2026-06-09-live-crypto-classic_regime-aware_hmm-top3_vwap-ema-bb_full-risk-f4.json9.5 KB

BTCUSD H1, five entry strategies scored under real crypto costs. Best run (regime-aware) 0.6% at 50% win rate; classic loses 11.1%.

BTCUSD-H1-2024-06-10-2026-06-09-legacy-zero-classic_regime-aware_hmm-top3_vwap-ema-bb_full-risk-f4.json9.4 KB

Same BTCUSD H1 window with zero costs and legacy fixed 2:1 sizing. The cost-free counterfactual to the live-crypto run above.

carry-validation-10majors-f4.json300 KB

Carry strategy across 10 majors with two-leg costs. All three variants (always-on, threshold, top-3 rotation) fail the deployment gate.

daily-momentum-validation-BTCUSD_ETHUSD_SOLUSD_BNBUSD_XRPUSD_ADAUSD_DOGEUSD_DOTUSD_LINKUSD_AVAXUSD-D1-f4.json141 KB

Daily time-series momentum across 10 crypto majors, D1. No config survives ~0.4% crypto costs robustly; the two positive symbols are flukes.

daily-momentum-validation-BTCUSD-D1-f4.json20 KB

Daily momentum isolated to BTCUSD, D1. Single-symbol slice of the majors validation for a focused read.

regime-hmm-walkforward-BTCUSD_ETHUSD_SOLUSD-H1-2024-06-12-2026-06-11.json13 KB

Three-state structural regime HMM (trend, volatile, range) on BTC/ETH/SOL H1, walk-forward. States price magnitude, not drift; no directional premium.

regime-routed-walkforward-BTCUSD_ETHUSD_SOLUSD-H1-2024-06-01-2026-06-01-f4.json52 KB

Per-regime routed entries on BTC/ETH/SOL H1 walk-forward. The only non-thin routed cell is negative on all three symbols; the gate fails on paper.

xsection-validation-30majors-D1-lb14-rb7-top5-f4.json9.3 KB

Cross-sectional momentum, 30 majors D1, top-5. Long-only and long-short both fail against the basket benchmark over the full window.

Use it

Three recipes against the live routes. Each references only fields that actually appear in the responses above.

1. Shell: count net-positive trades

# Count net-positive trades in the cost field (net R = grossR - costR)
curl -s https://tradeclaw.win/api/research/cost-field \
  | jq '[.grossR as $g | .costR as $c
         | range(0; .count)
         | select($g[.] - $c[.] > 0)] | length'

2. JavaScript: read the net edge per trade

// Load the cost-adjusted equity summary and print the net edge per trade
const res = await fetch(
  'https://tradeclaw.win/api/signals/equity?summaryOnly=1&scope=pro'
);
const { summary } = await res.json();

console.log('net expectancy  R/trade:', summary.netExpectancyR);
console.log('gross expectancy R/trade:', summary.expectancyR);
console.log('avg round-trip cost R:', summary.avgCostR);

3. Python: mean net R by asset class

# Load the cost field into a DataFrame and compute mean net R
import requests, pandas as pd

d = requests.get('https://tradeclaw.win/api/research/cost-field').json()
df = pd.DataFrame({
    't': d['t'],
    'grossR': d['grossR'],
    'costR': d['costR'],
    'cls': d['cls'],
})
df['netR'] = df['grossR'] - df['costR']
df['asset'] = df['cls'].map(dict(enumerate(d['classes'])))

print('mean net R/trade:', df['netR'].mean())
print(df.groupby('asset')['netR'].mean())

License and terms

There is no API key and no published rate limit on these read-only routes. As a courtesy, cache responses on your side rather than polling in a tight loop; the s-maxage headers make that cheap.

Informational only, not advice.

Every number here
is an endpoint
you can call.

Live endpoints

Committed research artifacts

Use it

License and terms

Read next

Every number here
is an endpoint
you can call.

Live endpoints

Committed research artifacts

Use it

License and terms

Read next