Comments (3)
Hi @none2003,
Thanks for pointing this out! The notebook should include an additional check that 20 bars have been completed:
def rank(ctxs: dict[str, ExecContext]):
if tuple(ctxs.values())[0].bars <= 20:
return
scores = {
symbol: ctx.indicator('roc_20')[-1]
for symbol, ctx in ctxs.items()
}
sorted_scores = sorted(
scores.items(),
key=lambda score: score[1],
reverse=True
)
threshold = pyb.param('rank_threshold')
top_scores = sorted_scores[:threshold]
top_symbols = [score[0] for score in top_scores]
pyb.param('top_symbols', top_symbols)
def rotate(ctx: ExecContext):
if ctx.bars <= 20:
return
if ctx.long_pos():
if ctx.symbol not in pyb.param('top_symbols'):
ctx.sell_all_shares()
else:
target_size = pyb.param('target_size')
ctx.buy_shares = ctx.calc_target_shares(target_size)
ctx.score = ctx.indicator('roc_20')[-1]
What's happening is that caching does not maintain the order of the rows of the original DataFrame. During the initial 20 bars of the backtest, the 20 day ROC score will be NaN, so the order of the scores will depend on the order that the symbols appear in the DataFrame. That ordering becomes non-deterministic once the cached data is read.
The issue is with the notebook code, not the caching mechanism. I have updated the notebook with the fix:
https://www.pybroker.com/en/latest/notebooks/10.%20Rotational%20Trading.html
Let me know if you need anything else.
from pybroker.
PyBroker does maintain the ordering of the dates, so the time sequence will be correct. However, the order in which the rows appear for different symbols on the same date does not have a guaranteed ordering.
from pybroker.
Hi @none2003,
Thanks for pointing this out! The notebook should include an additional check that 20 bars have been completed:
def rank(ctxs: dict[str, ExecContext]): if tuple(ctxs.values())[0].bars <= 20: return scores = { symbol: ctx.indicator('roc_20')[-1] for symbol, ctx in ctxs.items() } sorted_scores = sorted( scores.items(), key=lambda score: score[1], reverse=True ) threshold = pyb.param('rank_threshold') top_scores = sorted_scores[:threshold] top_symbols = [score[0] for score in top_scores] pyb.param('top_symbols', top_symbols)
def rotate(ctx: ExecContext): if ctx.bars <= 20: return if ctx.long_pos(): if ctx.symbol not in pyb.param('top_symbols'): ctx.sell_all_shares() else: target_size = pyb.param('target_size') ctx.buy_shares = ctx.calc_target_shares(target_size) ctx.score = ctx.indicator('roc_20')[-1]
What's happening is that caching does not maintain the order of the rows of the original DataFrame. During the initial 20 bars of the backtest, the 20 day ROC score will be NaN, so the order of the scores will depend on the order that the symbols appear in the DataFrame. That ordering becomes non-deterministic once the cached data is read.
The issue is with the notebook code, not the caching mechanism. I have updated the notebook with the fix: https://www.pybroker.com/en/latest/notebooks/10.%20Rotational%20Trading.html
Let me know if you need anything else.
Thank you for your quick action!
I'm a little confuse about "caching does not maintain the order of the rows of the original DataFrame", so how pybroker make sure the time sequence when get data from cache? Is data get resorted when retrieve from cache when using?
from pybroker.
Related Issues (20)
- enable_fractional_shares is not considered by calc_target_shares HOT 3
- Adding interactive examples
- Add cababillity to pickle the Portfolio object and then reuse it for incremental backtests or live trading HOT 14
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- How to limit the order price to satisfy "Limit up" or "Limit down" rule? HOT 1
- profit_factor below confidence interval HOT 1
- How to specify the closing price as the opening price of the next bar in Strategy.backtest()? HOT 1
- dependency conflicts HOT 1
- Suspicious short trade in a pure long trades HOT 6
- pip install lib-pybroker fail HOT 1
- "ValueError: DataSource is empty." for Akshare as data sources HOT 4
- Question about shares and pnl number in "result.trades" HOT 8
- can't backtest with fresh installation HOT 4
- Support Pandas 2 HOT 5
- Return indicator values and model predictions from Strategy HOT 1
- Add support for global models HOT 1
- Support custom fee structure HOT 3
- Add config option for disabling quantization in results HOT 1
- question about ctx.dt and indicator value HOT 3
- Error: Fill price 0 for prices below 0.005$ (like many cryptocurrencies) HOT 3
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from pybroker.