修正当前滚动因子日期取值
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boris
@@ -777,6 +777,19 @@ fn precomputed_stock_current_rolling_mean(
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}
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}
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fn is_precomputed_stock_current_rolling_key(key: &str) -> bool {
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fn has_numeric_window(key: &str, prefix: &str, suffix: &str) -> bool {
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key.strip_prefix(prefix)
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.and_then(|value| value.strip_suffix(suffix))
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.is_some_and(|window| {
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!window.is_empty() && window.bytes().all(|byte| byte.is_ascii_digit())
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})
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}
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has_numeric_window(key, "ma", "_current_close")
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|| has_numeric_window(key, "avg_volume", "_current")
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}
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pub struct PlatformExprStrategy {
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config: PlatformExprStrategyConfig,
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engine: Engine,
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@@ -3543,6 +3556,25 @@ impl PlatformExprStrategy {
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feature_market.volume as f64
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};
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let extra_factors = if self.stock_extra_factors_required {
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let mut values = factor.extra_factors.clone();
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if date != factor_date {
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values.retain(|key, _| !is_precomputed_stock_current_rolling_key(key));
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if let Some(current_factor) = ctx.data.factor(date, symbol) {
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values.extend(
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current_factor
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.extra_factors
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.iter()
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.filter(|(key, _)| is_precomputed_stock_current_rolling_key(key))
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.map(|(key, value)| (key.clone(), *value)),
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);
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}
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}
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values
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} else {
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BTreeMap::new()
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};
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let state = StockExpressionState {
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symbol: symbol.to_string(),
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market_cap,
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@@ -3603,11 +3635,7 @@ impl PlatformExprStrategy {
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stock_volume_ma20,
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stock_volume_ma60,
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stock_volume_ma100,
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extra_factors: if self.stock_extra_factors_required {
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factor.extra_factors.clone()
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} else {
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BTreeMap::new()
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},
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extra_factors,
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extra_text_factors: if self.stock_text_factors_required {
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ctx.data
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.factor_text_snapshots_on(date)
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@@ -28481,6 +28509,138 @@ mod tests {
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assert_eq!(stock.stock_volume_ma5, 1_000.0);
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}
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#[test]
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fn platform_current_rolling_uses_decision_date_row_not_prior_factor_row() {
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let factor_date = d(2025, 1, 6);
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let decision_date = d(2025, 1, 7);
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let symbol = "300001.SZ";
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let market_rows = [factor_date, decision_date]
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.into_iter()
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.map(|date| DailyMarketSnapshot {
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date,
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symbol: symbol.to_string(),
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timestamp: None,
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day_open: 10.0,
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open: 10.0,
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high: 10.2,
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low: 9.8,
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close: 10.0,
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last_price: 10.0,
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bid1: 9.99,
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ask1: 10.01,
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prev_close: 10.0,
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volume: 1_000,
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minute_volume: 1_000,
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bid1_volume: 1_000,
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ask1_volume: 1_000,
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trading_phase: None,
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paused: false,
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upper_limit: 11.0,
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lower_limit: 9.0,
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price_tick: 0.01,
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})
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.collect();
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let factor_rows = [(factor_date, 1.0, 100.0), (decision_date, 20.0, 2_000.0)]
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.into_iter()
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.map(
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|(date, current_close, current_volume)| DailyFactorSnapshot {
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date,
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symbol: symbol.to_string(),
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market_cap_bn: 20.0,
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free_float_cap_bn: 20.0,
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pe_ttm: 0.0,
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turnover_ratio: None,
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effective_turnover_ratio: None,
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extra_factors: BTreeMap::from([
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("adjustment_factor_backward1".to_string(), 1.0),
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("ma5_current_close".to_string(), current_close),
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("avg_volume5_current".to_string(), current_volume),
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]),
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},
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)
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.collect();
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let data = DataSet::from_components(
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vec![Instrument {
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symbol: symbol.to_string(),
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name: symbol.to_string(),
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board: "SZ".to_string(),
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round_lot: 100,
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listed_at: Some(d(2020, 1, 1)),
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delisted_at: None,
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status: "active".to_string(),
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}],
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market_rows,
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factor_rows,
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vec![CandidateEligibility {
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date: decision_date,
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symbol: symbol.to_string(),
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is_st: false,
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is_star_st: false,
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is_new_listing: false,
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is_paused: false,
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allow_buy: true,
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allow_sell: true,
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is_kcb: false,
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is_one_yuan: false,
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risk_level_code: None,
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}],
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vec![BenchmarkSnapshot {
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date: decision_date,
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benchmark: symbol.to_string(),
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open: 1_000.0,
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close: 1_002.0,
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prev_close: 998.0,
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volume: 1_000_000,
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}],
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)
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.expect("dataset");
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let portfolio = PortfolioState::new(100_000.0);
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let subscriptions = BTreeSet::new();
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let ctx = StrategyContext {
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execution_date: decision_date,
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decision_date,
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decision_index: 1,
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data: &data,
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portfolio: &portfolio,
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futures_account: None,
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open_orders: &[],
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dynamic_universe: None,
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subscriptions: &subscriptions,
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process_events: &[],
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active_process_event: None,
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active_datetime: None,
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order_events: &[],
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fills: &[],
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};
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let mut cfg = PlatformExprStrategyConfig::microcap_rotation();
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cfg.signal_symbol = symbol.to_string();
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cfg.prefer_precomputed_rolling_factors = true;
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cfg.stock_filter_expr = "rolling_mean_current(\"close\", 5) == 20.0 && rolling_mean_current(\"volume\", 5) == 2000.0".to_string();
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let strategy = PlatformExprStrategy::new(cfg);
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let stock = strategy
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.stock_state_with_factor_date(&ctx, decision_date, factor_date, symbol)
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.expect("stock state");
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let day = strategy.day_state(&ctx, decision_date).expect("day state");
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assert!(
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strategy
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.stock_passes_expr(&ctx, &day, &stock)
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.expect("current rolling filter")
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);
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assert_eq!(
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strategy
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.resolve_current_rolling_mean(&ctx, &day, Some(&stock), "close", 5)
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.expect("current close rolling mean"),
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20.0
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);
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assert_eq!(
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strategy
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.resolve_current_rolling_mean(&ctx, &day, Some(&stock), "volume", 5)
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.expect("current volume rolling mean"),
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2_000.0
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);
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}
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#[test]
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fn platform_stock_state_falls_back_when_precomputed_rolling_is_missing() {
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let current = d(2025, 5, 30);
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