# hpsilab.com: market-direction

> Probabilistic next-session US equity direction for AI trading agents, tracked on an immutable, publicly scored forward benchmark.

- Page: https://systemonemodels.ai/hpsilab/market-direction
- API: https://api.systemonemodels.ai/v1/models/hpsilab/market-direction

## Facts

| | |
|---|---|
| Maker | hpsilab.com (https://systemonemodels.ai/hpsilab) |
| Decides | noul |
| Architecture | other |
| Licence | Proprietary |
| Availability | Hosted API |
| Latest version | 0.1.0 |

## Hosted API

- Provider: HPSILab (https://hpsilab.com/api/ai_prediction)
- Docs: https://hpsilab.com/developer/v2
- Input: — · Output: —

## Model card

# HPSILab Market Direction

A probabilistic, forward-tracked market-direction decision model for AI trading agents.

## Overview

HPSILab Market Direction estimates the probability that a US equity will close higher on the next US trading session. It answers one typed question with a probability, so an agent can use the number directly — size a position by it, gate a trade on it, or combine it with its own signals — instead of parsing prose.

What sets it apart is the record behind the probabilities. HPSILab runs a public forward benchmark: predictions are made before the outcome exists, stored unchanged, scored at the next official close, and published with Brier score and a calibration table, wrong calls included.

## Publisher

HPSILab

HPSILab builds market decision infrastructure for AI trading agents.

Website: https://hpsilab.com/

## Decision primitive

`noul`

> Will {ticker} close higher on the next US trading session?

## Input

| Field | Where | Notes |
|---|---|---|
| `ticker` | URL path | US equity or ETF symbol, e.g. `NVDA`. A malformed symbol returns `400` with a suggested correction in the `X-HPSILAB-Suggested-Symbol` header; a well-formed symbol with no data returns `404`. |

There are no other inputs. The model reads the latest daily market state for the ticker itself.

```bash
curl https://hpsilab.com/api/ai_prediction/NVDA \
  -H "Authorization: Bearer $HPSI_API_KEY"
```

The same prediction is available to agents as the `get_ai_prediction` tool on the HPSILab MCP server (`https://hpsilab.com/mcp`). Authentication is an `hpsi_` API key sent as a Bearer token, or OAuth 2.1 for connectors that support it.

## Output

The REST endpoint returns a JSON array with one object. The MCP tool returns the same object without the array.

| Field | Type | Meaning |
|---|---|---|
| `ensemble_up_probability` | number, 0–1 | **The decision.** Probability that the next session closes higher. |
| `rf_up_probability` | number, 0–1 | Random-forest component of the ensemble. |
| `lr_up_probability` | number, 0–1 | Logistic-regression component of the ensemble. |
| `last_date` | string | Date of the last daily bar the prediction was made from. The prediction is for the session after it. |
| `last_close` | number | Close on `last_date`. |
| `model_variant` | string | Identifier of the model variant that produced it (currently `candidate`). |
| `daily_vol_est` | number or null | Estimated daily volatility. |
| `suggested_stop_loss_price` | number or null | Volatility-based reference level. |
| `suggested_take_profit_price` | number or null | Volatility-based reference level. |
| `sentiment_score` | number or null | News sentiment input, when available. |

## Example

Illustrative only — not a real prediction.

```json
[
  {
    "last_date": "2026-10-02",
    "model_variant": "candidate",
    "last_close": 100.0,
    "ensemble_up_probability": 0.54,
    "rf_up_probability": 0.56,
    "lr_up_probability": 0.52,
    "daily_vol_est": 0.021,
    "suggested_stop_loss_price": 97.9,
    "suggested_take_profit_price": 104.2,
    "sentiment_score": 0.1
  }
]
```

## Evaluation

### What is evaluated

HPSILab publishes an official forward benchmark at [hpsilab.com/accuracy](https://hpsilab.com/accuracy):

- **Benchmark:** US AI + Semiconductor Benchmark (`us-ai-semiconductor-v1`)
- **Universe:** 18 fixed tickers — NVDA, MSFT, GOOGL, META, AMZN, PLTR, AMD, ORCL, AI, SOUN, AVGO, QCOM, INTC, MU, TXN, MRVL, AMAT, ASML
- **Schedule:** one prediction per ticker at 16:15 New York time on every NYSE session, for the next NYSE session
- **Scoring:** at the next official close — direction, correct/incorrect, Brier score
- **Record:** predictions are written once and never edited; only predictions made inside the 16:15–16:45 window count

**The benchmark scores a separate, dedicated benchmark model (`benchmark-logit-v1`, a pooled logistic regression), not the production model behind this API.** It is kept separate so that the scoreboard cannot be tuned and so that every stored probability can be re-derived from the same price bars. Read its numbers as the forward record of HPSILab's direction modelling, not as a measurement of the exact responses this endpoint returns.

### Results

The benchmark started on 2026-10-03. Headline numbers are published once at least 100 predictions have been scored — the same rule the accuracy page applies — and will be added here at that point:

| Metric | Value |
|---|---|
| Accuracy | pending (fewer than 100 scored predictions) |
| Always-up baseline accuracy | pending |
| Brier score | pending |
| Calibration error (ECE, over the benchmark's calibration buckets) | pending |
| Calibration table (predicted vs. actual up-rate per bucket) | pending — live on [hpsilab.com/accuracy](https://hpsilab.com/accuracy) |
| Scored predictions | 0 as of 2026-10-04 |

### Latency

End-to-end HTTP latency of successful `GET /api/ai_prediction/{ticker}` requests in production over the 30 days to 2026-10-04 (2,655 requests, none excluded):

| Median | p95 |
|---|---|
| 254 ms | 1,534 ms |

This is server-side request time, measured by HPSILab's request logging. A ticker's first call for a new trading day computes the prediction; later calls return the stored one and are faster. Both are included.

## Why accuracy alone is insufficient

Next-session stock direction is close to a coin flip, and the share of up days varies by stock and by period. 80–90% accuracy figures from ordinary classification tasks are not a meaningful comparison. Judge this model on:

- **Accuracy against the always-up baseline** over the same scored sessions — beating 50% means little if the stocks rose 55% of the time.
- **Brier score** — rewards probabilities that are both right and appropriately confident.
- **Calibration** — when the model says 60%, does the stock go up about 60% of the time?
- **An out-of-sample forward record** — predictions fixed before the outcome, not a backtest.

## Intended use

- AI trading agents that need a probability rather than a narrative
- Research and signal evaluation
- Probabilistic decision systems that combine several inputs

## Limitations

- The output is a probability, not a guarantee; a high-probability call can still be wrong.
- Market regimes change, and a model fitted on past behaviour can degrade when they do.
- Direction accuracy ignores the size of moves and transaction costs; a profitable strategy needs more than a correct direction.
- The published benchmark scores a dedicated benchmark model on 18 AI and semiconductor stocks, not the production model across every ticker.
- For research and informational use. Not investment advice.

## Availability

Hosted API only. Weights are not published.

## Links

- Main site: [hpsilab.com](https://hpsilab.com)
- Developer docs: [hpsilab.com/developer/v2](https://hpsilab.com/developer/v2)
- MCP server: `https://hpsilab.com/mcp`
- Accuracy / benchmark: [hpsilab.com/accuracy](https://hpsilab.com/accuracy)
- Pricing: [hpsilab.com/pricing](https://hpsilab.com/pricing)

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