PIaaS — Trading Signal API for Python Developers

Send a price series. Get a structured BUY / SELL / HOLD signal with confidence score, risk score, and explainable reasoning.

PIaaS is a lightweight REST API that converts a price series into structured trading signals — no ML expertise or external data required.

Works with any price series — crypto, stocks, commodities, synthetic data.

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Example

curl -X POST http://localhost:8000/v1/predict \
  -H "X-API-Key: dev-key-1234" \
  -H "Content-Type: application/json" \
  -d '{"prices": [100, 101.5, 99.8, 102.3, 103.1, 101.9]}'

{
  "action": "HOLD",
  "confidence": 0.51,
  "expected_value": -0.01,
  "risk_score": 0.18,
  "signal_strength": 0.01,
  "reasoning_signals": [
    "positive momentum (+1.90%)",
    "price above MA5 (+0.55%)"
  ]
}

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How it works

prices[ ] → feature extraction → logistic regression → signal + reasoning

• Extracts momentum, volatility, and moving-average features from the last N prices
• Model trains once on first startup and is persisted to disk — subsequent starts are instant

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Why developers use this

• Algorithmic trading bots — call the API in a loop, no ML pipeline to build or maintain
• Financial dashboards — surface structured signals alongside raw price data
• Strategy prototyping — get a working signal endpoint in under 5 minutes

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Quickstart

git clone https://github.com/your-username/PIaaS
cd PIaaS
pip install -r requirements.txt
uvicorn main:app --reload

Default API key: dev-key-1234 — no config needed to start.

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Python SDK

from sdk.client import PIaaSClient

client = PIaaSClient("http://localhost:8000", api_key="dev-key-1234")
signal = client.predict([100, 101.5, 99.8, 102.3, 103.1, 101.9])

if signal["action"] == "BUY" and signal["confidence"] > 0.65:
    place_order(size=compute_position(signal["risk_score"]))

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Response fields

Field	Type	Description
action	BUY · SELL · HOLD	Recommended decision
confidence	0.0 – 1.0	Model confidence in the prediction
expected_value	float	P(up) − P(down)
risk_score	0.0 – 1.0	Volatility-based risk estimate
signal_strength	0.0 – 1.0	Distance from neutral (0 = no signal, 1 = strong)
reasoning_signals	list[str]	Human-readable explanation of the decision

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Why this is different

• deterministic — identical inputs always produce identical outputs — no stochastic inference
• local-first — zero network calls at inference time — runs entirely in your process
• transparent — logistic regression, not a black box — every signal is traceable to a feature
• self-contained — no third-party API key, no data broker, no external rate limit

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What this is not

• Not financial advice — outputs are developer signals, not investment recommendations
• Not a prediction guarantee — confidence scores reflect the model, not market certainty
• Not a managed service — designed to run in your own infrastructure

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Configuration

cp .env.example .env

Variable	Default	Description
API_KEYS	dev-key-1234	Comma-separated valid API keys
RATE_LIMIT	60/minute	Per-key rate limit

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API reference

Method	Path	Auth	Description
GET	/v1/health	—	Server and model status
POST	/v1/predict	X-API-Key header	Get a trading signal from a price series

Interactive docs: http://localhost:8000/docs

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License

MIT