Add Monte Carlo probability, earnings-blended forecasts, and AI analyst narratives to your platform — without building a quant team.
1{ 2 "summary": { 3 "macroRegime": "BEARISH", 4 "macroLabel": "mu x0.8 sigma x1.1", 5 "pathsSimulated": 10000, 6 "diffusionModel": "Student-t (df=5)" 7 }, 8 "assets": [{ 9 "symbol": "AAPL",10 "price": 273.17,11 "verdict": "Roughly flat",12 "expectedReturnPct": 0.14,13 "probabilityHit": 0.4790,14 "baseProbabilityHit": 0.0151,15 "riskProfile": {16 "pLose5": 0.2634,17 "pLose10": 0.0999,18 "pLose20": 0.003519 },20 "earnings": {21 "inHorizon": true,22 "blendFormula": "(1-0.79) x 1.5% + 0.79 x 60.0%",23 "probabilityAfterEarnings": 0.479024 },25 "aiEnrichment": {26 "analystVerdict": "Apple is in a steady-state product cycle...",27 "catalysts": ["iPhone 18 Pro upgrade cycle", ...],28 "risks": ["CEO transition in September...", ...]29 }30 }]31}Everything a developer needs to know in one block. The full reference doc is shared after request access.
POST /api/simulate-full~4,500 US-listed equities (NYSE + Nasdaq)All 11 GICS, with per-sector news enrichment7, 15, 21, 30, 60, 90 trading days19 discrete targets per horizon, from 1% to 500%5%, 10%, 20%, 30%, 50% (precomputed per stock)Nightly end-of-day batch10,000 paths per stock per horizonStudent-t with 5 degrees of freedom (fat-tailed)Single structured JSON, all derivations precomputedCached probability (sub-100ms) and AI-enrichedWe run 10,000-path Monte Carlo on every covered stock, every night, across 6 horizons and 19 targets. You hit one endpoint and get the full distribution back as JSON. The math is solved. You ship features.
4,500+ US equities, refreshed nightly
Pick speed or pick depth — same endpoint
Every input, every multiplier, every blend exposed
AAPL · +20% target · 30 trading days · enriched mode. Captured from the production endpoint.
1curl -X POST https://api.atlas-stocks.com/api/simulate-full \ 2 -H "Content-Type: application/json" \ 3 -d '{ 4 "symbols": ["AAPL"], 5 "targetReturnPct": 20, 6 "horizonDays": 30, 7 "skipNews": false 8 }'Tap any layer to see the formula
Every response includes a complete downside distribution at five drawdown thresholds. Always monotonically non-increasing — if pLose20 is 0.5%, pLose30 cannot exceed it.
Real AAPL · 30-day horizon · +20% targetEvery response includes a keyDrivers object with four UI-ready blocks. Each has a label, a direction tag (positive / neutral / negative), and a descriptive blurb. Drop them into a four-up card layout — no client-side classification logic needed.
riskLevelClassifies the stock's volatility against typical equity benchmarks. Derived from annualizedSigmaPct.
σ > 35%→20% < σ ≤ 35%→σ ≤ 20%→trendDirectional bias of the expected return. Derived from expectedReturnPct.
expected > 1%→−1% ≤ exp ≤ 1%→expected < −1%→earningsImpactBinary — earnings either fall inside the horizon or they don't. When they do, dir is set by the historical hit rate.
No earnings in horizon→In horizon · hist > 15%→In horizon · hist ≤ 15%→newsImpactReflects the news sentiment adjustment applied to the probability. Derived from globalScore.
globalScore > 0.1→−0.1 ≤ score ≤ 0.1→globalScore < −0.1→The pattern that minimizes latency and maximizes perceived responsiveness. Most clients render the cached response in under 100ms, then progressively populate the analyst narrative when it arrives.
skipNews: trueWhen a user opens a stock page, request the cached response. It returns in under 100ms. Render probability, riskProfile, verdict, keyDrivers, full transparency block. The UI is fully populated.
skipNews: falseSimultaneously, request the AI-enriched response. On a cold cache it can take 8–12s. While it loads, show a subtle skeleton in the "Analyst view" section. Don't block the rest of the UI.
data.aiEnrichmentWhen the enriched response lands, fill in analystVerdict, catalysts, risks, newsContext. Subsequent stock opens within 4–5 hours hit cache and return both calls in <100ms.
Everything you need to know before integrating.
400401429500503If you serve retail investors and you don't already have a quant team, ATLAS is the fastest way to ship probability features.
Risk-aware allocation suggestions
Pre-trade probability context
Education + research surfaces
No public price list. Cached-mode and enriched-mode have different unit economics; we size pricing to your traffic and your tier mix. Fill the form and we'll come back with a number within 48 hours.
Fill this out and we'll come back within 48 hours with a sandbox key and a pricing proposal.
You ship features. We solve the math.
Request access