zsb/fidc-backtest-engine
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

修复预计算rolling缺失语义

Browse Source
This commit is contained in:
boris
2026-07-07 23:05:26 +08:00
parent 22451300b1
commit 5a1534e51e
1 changed files with 156 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files
crates/fidc-core/src/platform_expr_strategy.rs
+156 -17
View File
@@ -545,20 +545,28 @@ fn precomputed_stock_rolling_mean(
field: &str,
lookback: usize,
) -> Option<f64> {
let keys: &[&str] = match (field.trim().to_ascii_lowercase().as_str(), lookback) {
("close" | "prev_close" | "stock_close" | "price", 5) => &["ma5_prev_close", "ma5"],
("close" | "prev_close" | "stock_close" | "price", 10) => &["ma10_prev_close", "ma10"],
("close" | "prev_close" | "stock_close" | "price", 20) => &["ma20_prev_close", "ma20"],
("close" | "prev_close" | "stock_close" | "price", 30) => &["ma30_prev_close", "ma30"],
("volume" | "stock_volume", 5) => &["avg_volume5", "vma5"],
("volume" | "stock_volume", 20) => &["avg_volume20", "vma20"],
("volume" | "stock_volume", 60) => &["avg_volume60", "vma60"],
("volume" | "stock_volume", 100) => &["avg_volume100", "vma100"],
_ => return None,
if lookback == 0 {
return None;
}
let value_for = |key: &str| {
extra_factors
.get(key)
.copied()
.filter(|value| value.is_finite())
};
keys.iter()
.find_map(|key| extra_factors.get(*key).copied())
.filter(|value| value.is_finite())
match field.trim().to_ascii_lowercase().as_str() {
"close" | "prev_close" | "stock_close" | "price" => {
let primary = format!("ma{lookback}_prev_close");
let alias = format!("ma{lookback}");
value_for(&primary).or_else(|| value_for(&alias))
}
"volume" | "stock_volume" => {
let primary = format!("avg_volume{lookback}");
let alias = format!("vma{lookback}");
value_for(&primary).or_else(|| value_for(&alias))
}
_ => None,
}
}
pub struct PlatformExprStrategy {
@@ -2584,10 +2592,7 @@ impl PlatformExprStrategy {
) -> Option<f64> {
let precomputed = precomputed_stock_rolling_mean(extra_factors, field, lookback);
if self.config.prefer_precomputed_rolling_factors {
precomputed.or_else(|| {
ctx.data
.market_decision_numeric_moving_average(date, symbol, field, lookback)
})
precomputed
} else {
let computed = ctx
.data
@@ -23922,6 +23927,130 @@ mod tests {
assert_eq!(stock.stock_volume_ma5, 1_000.0);
}
#[test]
fn platform_stock_state_keeps_missing_precomputed_rolling_as_missing() {
let current = d(2025, 5, 30);
let start = current - chrono::Duration::days(100);
let symbol = "300022.SZ";
let market_rows: Vec<DailyMarketSnapshot> = (0..=100)
.map(|idx| {
let date = start + chrono::Duration::days(idx);
DailyMarketSnapshot {
date,
symbol: symbol.to_string(),
timestamp: Some(format!("{date} 10:15:00")),
day_open: 10.0,
open: 10.0,
high: 10.2,
low: 9.8,
close: 10.0,
last_price: 10.0,
bid1: 9.99,
ask1: 10.01,
prev_close: 9.9,
volume: 1_000,
minute_volume: 1_000,
bid1_volume: 1_000,
ask1_volume: 1_000,
trading_phase: Some("continuous".to_string()),
paused: false,
upper_limit: 11.0,
lower_limit: 9.0,
price_tick: 0.01,
}
})
.collect();
let benchmark_rows: Vec<BenchmarkSnapshot> = market_rows
.iter()
.map(|row| BenchmarkSnapshot {
date: row.date,
benchmark: "000852.SH".to_string(),
open: 1000.0,
close: 1002.0,
prev_close: 998.0,
volume: 1_000_000,
})
.collect();
let data = DataSet::from_components(
vec![Instrument {
symbol: symbol.to_string(),
name: symbol.to_string(),
board: "SZ".to_string(),
round_lot: 100,
listed_at: Some(d(2020, 1, 1)),
delisted_at: None,
status: "active".to_string(),
}],
market_rows,
vec![DailyFactorSnapshot {
date: current,
symbol: symbol.to_string(),
market_cap_bn: 20.0,
free_float_cap_bn: 20.0,
pe_ttm: 0.0,
turnover_ratio: None,
effective_turnover_ratio: None,
extra_factors: BTreeMap::from([("ma5_prev_close".to_string(), 10.0)]),
}],
vec![CandidateEligibility {
date: current,
symbol: symbol.to_string(),
is_st: false,
is_star_st: false,
is_new_listing: false,
is_paused: false,
allow_buy: true,
allow_sell: true,
is_kcb: false,
is_one_yuan: false,
risk_level_code: None,
}],
benchmark_rows,
)
.expect("dataset");
let portfolio = PortfolioState::new(100_000.0);
let subscriptions = BTreeSet::new();
let ctx = StrategyContext {
execution_date: current,
decision_date: current,
decision_index: 100,
data: &data,
portfolio: &portfolio,
futures_account: None,
open_orders: &[],
dynamic_universe: None,
subscriptions: &subscriptions,
process_events: &[],
active_process_event: None,
active_datetime: None,
order_events: &[],
fills: &[],
};
let mut cfg = PlatformExprStrategyConfig::microcap_rotation();
cfg.signal_symbol = symbol.to_string();
cfg.prefer_precomputed_rolling_factors = true;
cfg.stock_filter_expr = "rolling_mean(\"volume\", 100) > 0".to_string();
let strategy = PlatformExprStrategy::new(cfg.clone());
let stock = strategy
.stock_state_with_factor_date(&ctx, current, current, symbol)
.expect("stock state");
assert!(stock.stock_volume_ma100.is_nan());
let day = strategy.day_state(&ctx, current).expect("day state");
assert!(
!strategy
.stock_passes_expr(&ctx, &day, &stock)
.expect("stock expr")
);
cfg.prefer_precomputed_rolling_factors = false;
let strategy = PlatformExprStrategy::new(cfg);
let stock = strategy
.stock_state_with_factor_date(&ctx, current, current, symbol)
.expect("stock state");
assert_eq!(stock.stock_volume_ma100, 1_000.0);
}
#[test]
fn precomputed_rolling_mean_ignores_strategy_specific_v104_labels() {
let mut extra_factors = BTreeMap::new();
@@ -23938,16 +24067,26 @@ mod tests {
);
extra_factors.insert("ma5_prev_close".to_string(), 10.5);
extra_factors.insert("ma40_prev_close".to_string(), 11.5);
extra_factors.insert("avg_volume100".to_string(), 120_000.0);
extra_factors.insert("avg_volume40".to_string(), 40_000.0);
assert_eq!(
precomputed_stock_rolling_mean(&extra_factors, "close", 5),
Some(10.5)
);
assert_eq!(
precomputed_stock_rolling_mean(&extra_factors, "close", 40),
Some(11.5)
);
assert_eq!(
precomputed_stock_rolling_mean(&extra_factors, "volume", 100),
Some(120_000.0)
);
assert_eq!(
precomputed_stock_rolling_mean(&extra_factors, "volume", 40),
Some(40_000.0)
);
}
#[test]