One more signal in your stack. One you can actually trust.
You're always hunting for the next uncorrelated input, and most of it turns out to be noise. Pearlixa is an independent quant model you can plug into the process you already run. Every call comes back with a calibrated confidence you can weight on, plus an entry, target and stop.
import { Pearlixa, Horizon } from "@pearlixa/sdk"
const px = new Pearlixa({
apiKey: process.env.PEARLIXA_KEY,
})
const signal = await px.getPrediction(
"BTC", Horizon.MID,
){
action: "BUY",
confidence: 0.87,
entry_price: 94_250,
take_profit: 98_400,
stop_loss: 91_100,
}Your bot already works. Your data, your models, your execution are dialed in. Pearlixa adds a second, independent quant analysis to weigh alongside your own.
A directional call with entry, target, stop, and a calibrated confidence, in one request.
Never a blind signal. You always see how sure the model is.
Every call ships with the model's own confidence, so you're never acting blind. A 0.87 is high conviction. A 0.55 is the model telling you it isn't sure.
You set the bar: take what clears it, and size the rest by how sure the model is. That number is what turns a tip into something you can gate, size, and blend on.
See the methodologyYou see every signal's conviction and act only on what clears your bar.
Confidence is calibrated to mean what it says. Live figures publish at launch.
An edge you add. Not a system you adopt.
Blending it in takes a few lines, not a rebuild. Here are four patterns teams use, from a light confirmation check to feeding the signal straight into a model.
Confirmation filter
Gate your own signals so they only fire when Pearlixa agrees and its confidence clears your bar.
Position sizing
Let the calibrated confidence set the size, so a 0.87 setup gets more than a 0.62 one.
Ensemble feature
Feed the direction and confidence into your model as one more input, next to your own features.
Diversifying alpha
An independent signal to diversify the edges you already run. Low correlation, more robust.
Looks simple. Does a lot more.
The single call is the easy path. Batch your whole watchlist in one request, get a full setup back for every name, and rank them however your strategy wants. A market screener in a dozen lines.
1import { Pearlixa, Horizon } from "@pearlixa/sdk"2 3const px = new Pearlixa({ apiKey: process.env.PEARLIXA_KEY })4 5// one request scans the whole watchlist6const { predictions } = await px.getBatchPredictions(7 watchlist, Horizon.MID,8)9 10// your bar: keep the conviction, ranked11const setups = predictions12 .filter(p => p.confidence >= 0.80)13 .sort((a, b) => b.confidence - a.confidence)- Batch your whole watchlist in one request
- Short, mid and long horizons
- Stream new signals over WebSocket
- Filter and rank it however your strategy wants