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Zipline Reloaded — event-driven backtesting engine. Minute and daily data, custom factors, pipeline API, risk and performance analytics. Forked from Quantopian's Zipline for continued development.
Passed 17 of 22 test cases with the skill, and 16 of 22 without it.
1 fixed16 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
Inside a daily bar handler, what exact expression queries a 50-bar window of daily price data for an asset and calculates its arithmetic mean?
| Checked | Without skill | With skill |
|---|---|---|
| Calls data.history(context.asset, "price", 50, "1d").mean() | Missed | Passed |
The instructions your model reads when the skill loads.
Zipline Reloaded is an event-driven backtesting engine (forked from Quantopian). Supports minute and daily data, custom factors, pipeline API, and built-in risk/performance analytics.
uv pip install zipline-reloaded
from zipline.api import order_target, symbol
from zipline import run_algorithm
def initialize(context):
context.asset = symbol("AAPL")
def handle_data(context, data):
price = data.current(context.asset, "price")
sma20 = data.history(context.asset, "price", 20, "1d").mean()
sma50 = data.history(context.asset, "price", 50, "1d").mean()
order_target(context.asset, 100 if sma20 > sma50 else 0)
results = run_algorithm(start=pd.Timestamp("2022-01-01"), end=pd.Timestamp("2023-01-01"),
initialize=initialize, handle_data=handle_data, capital_base=10000)
test cases passed, vs 16 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.