outcometick

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)

What is in here

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.

Testing

pip install . && python -m unittest discover -s tests

Downloading data

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 verified

from_ 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.

Rebuilding a Polymarket order book

book and price_change are stored at a capture cadence, so a removal can fall between two stored frames and leave a stale level behind. OrderBook applies the documented rebuild rule: snapshots replace a token's ladder, changes set absolute sizes (0 removes), and levels crossed by the newest best bid/ask (from best_bid_ask and from each change) are dropped. Feed it rows from the three files merged by recv_ms.

from outcometick.data import OrderBook

book = OrderBook()
for row in rows:
    book.apply(row)        # dicts or JSONL lines
book.ladder(token_id)      # {"bids": [{"price", "size"}], "asks": [...]}, best first
book.best(token_id)        # {"bid": ..., "ask": ...}

That removes every level the best prices have moved past, but it cannot restore what the dropped frames added or resized: until the next snapshot a level, at the top too, can be missing or carry an old size (best_bid_ask has prices only). best() is the best level of the rebuilt ladder, not the market's latest best bid/ask — read best_bid_ask rows for that.

Smart-money trade history

A separate subscription with its own key: daily files of the trades made by the top-ranked Polymarket traders.

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("top1000", {}).get("status") == "published"), None)
if day:
    smart.smart_download(day, "top1000", save_to="top1000.csv.zst")   # verified

What 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.

Running a backtest

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

Links

• 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
• Discord (English and 中文) and Telegram — questions and strategy talk