The Python strategy SDK for outcometick.com — tick-level data for Polymarket and Predict.fun crypto Up/Down markets.
pip install outcometick
from outcometick import Strategy, Order
class MeanReversion(Strategy):
def on_market_open(self, ctx, market):
self.entered = False
def on_tick(self, ctx, tick):
z = ctx.zscore(tick.value, window=180)
if self.entered or abs(z) < ctx.p.entry_z:
return None
side = "DOWN" if z > 0 else "UP"
limit = ctx.book().best(side)
if limit is None:
return None
self.entered = True
return Order(side=side, size=ctx.p.size, limit=limit)The SDK surface your strategy imports, and nothing else:
Strategy |
the base class you subclass |
Order |
what a hook returns; validates side, size and limit on construction |
SIDES |
("UP", "DOWN") |
It is typed (py.typed), so your editor and mypy know the API. Everything a
strategy can actually do arrives through ctx, which the runner constructs —
there is deliberately nothing here to reach out with.
The hooks are not defined on the base class on purpose. A default no-op
on_tick would turn "you declared a hook you did not implement" — a rejection
fixable in seconds — into a run that quietly never trades and bills you for an
empty equity curve.
pip install . && python -m unittest discover -s tests
The other half of the package, on a separate import because it has nothing to do with writing a strategy:
from outcometick.data import DataClient, NO_VALUE
ot = DataClient() # key from OT_KEY
meta = ot.meta() # what can this key see?
res = ot.files(
from_="2026-08-01", to="2026-08-12", # or date="2026-08-12"
asset=["BTC", "ETH"], # the BASE symbol, not BTCUSD
dataset="prices",
interval=["5m", NO_VALUE], # "5m" alone EXCLUDES the
) # period-less settlement streams
ot.download(res["files"][0], save_to="btc.csv.gz") # checksum verifiedfrom_ rather than from, because from is a Python keyword; it goes on the
wire as from.
meta()["intervals"] holds real durations only — the none sentinel is
reported separately under filterTokens, so code that builds an enum from it
or parses the values as durations never meets a token.
A separate subscription with its own key: daily files of the trades made by the top-ranked Polymarket traders. Until it is on sale these calls answer 503.
import os
smart = DataClient(key=os.environ["OT_SMART_KEY"])
days = smart.smart_days()["days"] # newest first
day = next((d["day"] for d in days if d["lists"].get("top100", {}).get("status") == "published"), None)
if day:
smart.smart_download(day, "top100", save_to="top100.csv.zst") # verifiedWhat the lists are and what each column means: https://outcometick.com/polymarket-smart-money-data
Standard library only: no requests, no dependency added to your project.
Submitting and replaying is done with the ot command line, which is
distributed on npm because there is exactly one of it for both languages:
npm i -g outcometick
ot check . # the same validator the queue runs
ot run . # replay locally against sample data
ot submit . --assets btc --days 30 # send it to the queue
Backtests cover only the most recent 35 archived days (breaking in
2.0); an earlier range is refused with E_SCOPE, naming the current window.
It runs Python strategies by spawning your local python3. Two CLIs would mean
two copies of the validator, and the second copy is what makes
"if it passes locally it will not be rejected on submit" stop being true.
Full reference: https://outcometick.com/docs/sdk
- outcometick.com — what this is, and what the data covers
- Run a backtest — paste a strategy, watch it run
- SDK reference — manifest, hooks,
ctx, limits - Data API — the archive these strategies read