Bring your own rules — or your own Python. Run them against SPX, NDX and 40+ single names, minute by minute, from 2008 to today. You see the estimated cost before every run, and right now every run is free.
Build entry and exit rules in the browser, or upload a Python file against a small, documented interface. Your logic, not a fixed menu of presets.
Minute bars back to 2008 for SPX, NDX and 40+ liquid single names, split- and dividend-adjusted — plus SPX options chains where the history supports it.
Every job is priced before you commit: date range, universe and resolution give you an estimate in dollars and minutes. Nothing starts until you say go.
Four steps, no infrastructure to think about.
Write rules in the builder or upload a Python strategy file.
Pick your window and universe. We price the run before it starts.
The job lands on a dedicated spot instance with a hard time limit.
Equity curve, drawdown, trade log and per-year breakdown — exportable.
The classic RSI(2) mean reversion on SPX — the same strategy shown above. This is the whole file.
# rsi2_meanrev.py — runs as-is from dfd import Strategy, indicators class RSI2(Strategy): symbol = "SPX" interval = "1d" def setup(self, bars): self.rsi = indicators.rsi(bars.close, 2) self.ma = indicators.sma(bars.close, 200) def on_bar(self, i): # only trade with the long-term trend if self.close[i] < self.ma[i]: return if self.rsi[i] < 10 and not self.position: self.buy() elif self.rsi[i] > 70 and self.position: self.close_position()
Illustrative figures for a documented public strategy, shown to demonstrate the output format. Your results will differ. Backtested performance is hypothetical and is not a guide to future returns.
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