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@@ -45,7 +45,7 @@ def main() -> None:
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commodities.sort(reverse=True)
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for m in commodities:
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print(f'{m.full_ticker:>8} {m.bid:5} {m.ask:5} {m.spread*100: 5.0f}% {m.chart_analysis.bids_filled:12.0f} '
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- f'{m.chart_analysis.asks_filled:12.0f} {m.chart_analysis.profits:10.0f} {m.chart_analysis.p75_fill_time}')
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+ f'{m.chart_analysis.asks_filled:12.0f} {m.chart_analysis.profits:10.0f} {format_td(m.chart_analysis.p75_fill_time)}')
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print()
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def check_cxos() -> None:
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@@ -82,10 +82,13 @@ def analyze_price_chart(exchange_ticker: str, midpoint: float) -> ChartAnalysis:
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if p['Interval'] == 'MINUTE_FIVE']
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pcpoints.reverse()
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five_min = 5 * 60 * 1000
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+ cutoff = datetime.datetime.now(datetime.UTC) - datetime.timedelta(days=30)
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asks_filled: list[AskFilled] = []
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fill_time = []
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r = ChartAnalysis(bids_filled=0, asks_filled=0, profits=0, p75_fill_time=datetime.timedelta.max)
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for hist in pcpoints:
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+ if datetime.datetime.fromtimestamp(hist['DateEpochMs'] // 1000, datetime.UTC) < cutoff:
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+ continue
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time = hist['DateEpochMs'] // five_min
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bids = asks = 0
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if hist['Low'] > midpoint:
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@@ -129,6 +132,13 @@ def analyze_price_chart(exchange_ticker: str, midpoint: float) -> ChartAnalysis:
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r.p75_fill_time = statistics.quantiles(fill_time, n=4)[2] * datetime.timedelta(minutes=5)
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return r
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+def format_td(td: datetime.timedelta) -> str:
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+ if td == datetime.timedelta.max:
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+ return '∞'
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+ days, seconds = divmod(td.total_seconds(), 24 * 60 * 60)
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+ hours = seconds / (60 * 60)
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+ return f'{int(days)}d {hours:4.1f}h'
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+
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class ExchangeOrder(typing.TypedDict):
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MaterialTicker: str
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ExchangeCode: str
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