Your ACE in the market

Ultimate Toolkit
for Traders and Quants

All-in-one quantitative toolkit for Python and JavaScript.
🛠️ Blazingly-fast Open Source Library
Run PineScript from Python / JS
🤖 Bot automation - HFT ready
📈 70+ Technical-Analysis indicators
🧠 AI-powered insights and signals
🔍 Blockchain wallet analytics
🗣️ Sentiment analysis
🧮 Parameter optimization & hyper-search
From research to profitable production.

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BTC/USD

15m

$42,385.21+2.4%

Used by

AlgoAlpha

Mine edge from the market

Use Cases

Extract features, backtest, and execute trading ideas in one toolkit.
Code once, deploy anywhere - Python, JS, or the browser - all powered by the same high-performance Rust engine.

Open Source Library

Blazingly-fast Rust core for Python, Node.js, and the browser.

70+ Technical Analysis indicators
Extremely fast backtesting engine
Backtest reports - Net Profit, Win Rate, Sharpe Ratio, etc.
Interoperable with Pandas and Polars
Cross-language, cross-platform
Fully typed API
OHLCV resampling, aggregation, bars

oss.py

import qpace as qp

ohlcv = qp.Ohlcv.read_csv("btc.csv", "15m")
ctx = qp.Ctx(ohlcv, "BTC_USD")

rsi = qp.ta.rsi(ctx, src=ohlcv.close, length=14)
bt = qp.Backtest(ctx)

for bar_index in bt:
    if rsi[bar_index] < 30:
        bt.signal(qp.Signal.Long())
    elif rsi[bar_index] > 70:
        bt.signal(qp.Signal.Short())
  
bt.display()

Feature Engineering

Mine your edge from the data.

Write indicators once - in PineScript
Compile PineScript to Python and JS
High performance, low latency - HFT ready
Use indicators as features in strategies / ML models
Research and prototyping

indicator.pine

//@version=5
library("PineLibrary")

export gaussian_kernel(series float src, int lookback) =>
    var float sum = 0.0
    var float weight_sum = 0.0
    for i = 0 to lookback - 1
        weight = exp(-i * i / (2 * lookback * lookback))
        sum += src[i] * weight
        weight_sum += weight
    sum / weight_sum

ACE Signals

The AI-powered market insights you can't afford to ignore.

Actionable signals
Confidence scores
Regime detection
Pattern recognition
Sentiment analysis

ace_signals.py

signals = qp.signals(sym="BTC_USD", timeframe="1D")

pprint(signals)
{
    "micro_trend": { "up": 0.8, "down": 0.1, "neutral": 0.1 },
    "entry_3:1": { "long": 0.75, "short": 0.25 },
    "sentiment": { "positive": 0.7, "negative": 0.2, "neutral": 0.1 },
    "double_top": 0.10,
    "head_and_shoulders": 0.05,
}

Execution

From research to profitable production.

Seamless signal execution
Live OHLCV data
Real-time signal execution
Discord and Telegram integration

execution.py

import my_library as pine

exchange = qp.Exchange("binance")
ohlcv = qp.ohlcv("BTC_USD", "15m", live=True)

def on_bar(bar):
    signal = pine.my_strategy(ctx)
    exchange.signal(signal)
    discord.signal(signal)
    telegram.signal(signal)
  
ohlcv.on_bar(on_bar)