perpsignal 0.1.0

A backtestable signal engine for perpetual-futures strategies — DSL, indicators, and an honest backtester.

perpsignal

A backtestable signal engine for perpetual-futures strategies. Write a strategy as a compact expression (or a JSON signal definition), evaluate it against OHLCV data, and backtest it into honest metrics — Sharpe, return, drawdown, win rate — with fees, funding, stops/targets, and leverage modelled.

This is the open-source core extracted from Signalview, a non-custodial platform where backtested perps strategies are scored and traded by AI agents on Hyperliquid. The engine here has no wallet, key, custody, or live-trading code — it's a pure research/backtest library, safe to run anywhere.

⚠️ Research and backtesting only. Nothing here is financial advice, and a backtest is not a promise of future results. Perpetual-futures trading is high-risk.

Install

pip install perpsignal          # once published
# or, from source:
pip install git+https://github.com/mokshyaprotocol/signalview

Requires Python 3.10+, pandas, numpy, requests.

Quickstart

import pandas as pd
from perpsignal import evaluate, discretize, run, BacktestConfig, RiskConfig

# df needs columns: open, high, low, close, volume (a DatetimeIndex is ideal)
df = pd.read_parquet("BTCUSDT-1h.parquet")

cfg = BacktestConfig(symbol="BTCUSDT", interval="1h")  # holds the entry thresholds

# A score is any expression that evaluates to a Series (positive = long).
# This one is a mean-reversion fade: high when price is stretched below its
# 48-bar mean, low when stretched above.
score = evaluate("zscore(close, 48) * -1", df)

# Map the continuous score to a {-1, 0, +1} position via the config thresholds.
# run() expects a discrete position, not a raw score.
position = discretize(score, cfg)

result = run(
    df, position, cfg,
    RiskConfig(),            # leverage / take-profit / stop-loss (sane defaults)
    bars_per_year=24 * 365,  # hourly bars
)
print(result.metrics)   # {'sharpe': ..., 'total_return': ..., 'max_drawdown': ..., 'win_rate': ..., 'trades': ...}

The signal DSL

Expressions are built from market variables and functions.

Variables: close, high, low, open, volume, funding, oi (open interest), bar_index.

Functions:

Trend / momentum	rsi(close, n), ema(close, n), sma(close, n), slope(close, n), adx(n), macd-style via ema diffs
Volatility	atr(n), stdev(x, n), bb_width(close, n, k), bb_upper/bb_lower/bb_mid(close, n, k)
Normalization	zscore(x, n), clip(x, lo, hi), sign(x), abs(x), log(x), sqrt(x), tanh(x)
Volume / flow	vwap(n), session_vwap(), corr(a, b, n)
Windowing	highest(x, n), lowest(x, n), prev(x, k)
Logic	if(cond, a, b), min, max, comparisons (>, <, >=, ...), and/or

Expressions are parsed by a real tokenizer→parser→evaluator (perpsignal.dsl) and include an auto-repair pass (parse_with_repair) that fixes common mistakes and reports what it changed — handy when the author is an LLM.

Signals as data

A strategy can also be a JSON SignalDef (portable, diffable, PR-able):

from perpsignal import parse_signal
sig = parse_signal({
    "asset": "BTCUSDT", "timeframe": "1h",
    "expression": "rsi(close, 14) - 50",
    # ... weights, regime config, risk bounds
})

See examples/ for runnable scripts and a sample signal file.

Fetching data

perpsignal.data pulls public Binance / Hyperliquid OHLCV and caches to disk. An optional Upstash cache layer self-disables when its env vars are unset — you never need it to run locally.

Contributing

New indicators, factors, and strategies are very welcome — this engine exists to be extended. A new built-in is usually one function plus one test. See CONTRIBUTING.md. Every PR is backtested in CI so improvements are judged on objective metrics, not opinion — which also makes this a clean target for autonomous/AI-agent strategy search.

License

Apache License 2.0. © 2026 Signalview.