接通真实昨收成交额并前置校验表达式字段

Cargo.lock

@@ -192,6 +192,7 @@ dependencies = [
  "serde",
  "serde_json",
  "sha2",
+ "ta-lib",
  "thiserror",
 ]
 
@@ -537,6 +538,19 @@ dependencies = [
  "unicode-ident",
 ]
 
+[[package]]
+name = "ta-lib"
+version = "0.8.1"
+source = "git+https://github.com/TA-Lib/ta-lib.git?rev=dd5a90259a3f9e04e2da9f38bf0719a841b40108#dd5a90259a3f9e04e2da9f38bf0719a841b40108"
+dependencies = [
+ "ta-lib-dispatch",
+]
+
+[[package]]
+name = "ta-lib-dispatch"
+version = "0.1.2"
+source = "git+https://github.com/TA-Lib/ta-lib.git?rev=dd5a90259a3f9e04e2da9f38bf0719a841b40108#dd5a90259a3f9e04e2da9f38bf0719a841b40108"
+
 [[package]]
 name = "thin-vec"
 version = "0.2.16"

crates/fidc-core/Cargo.toml

@@ -15,3 +15,4 @@ serde.workspace = true
 serde_json.workspace = true
 sha2.workspace = true
 thiserror.workspace = true
+ta-lib = { git = "https://github.com/TA-Lib/ta-lib.git", rev = "dd5a90259a3f9e04e2da9f38bf0719a841b40108" }

crates/fidc-core/examples/factor-event-kernel.rs

@@ -0,0 +1,35 @@
+use fidc_core::factor_events::{self, Expr, Frame};
+use serde::Deserialize;
+use serde_json::{Value, json};
+use std::io::{self, Read};
+
+#[derive(Deserialize)]
+#[serde(deny_unknown_fields)]
+struct Request {
+    expressions: std::collections::BTreeMap<String, Expr>,
+    frame: Frame,
+}
+
+fn main() -> Result<(), Box<dyn std::error::Error>> {
+    let mut input = String::new();
+    io::stdin().read_to_string(&mut input)?;
+    let output = if input.trim().is_empty() {
+        factor_events::catalog()
+    } else {
+        let request: Request = serde_json::from_str(&input)?;
+        let results = request
+            .expressions
+            .iter()
+            .map(|(id, expr)| {
+                let result = match factor_events::evaluate(expr, &request.frame) {
+                    Ok(v) => json!({"result":v}),
+                    Err(e) => json!({"error":e}),
+                };
+                (id.clone(), result)
+            })
+            .collect::<std::collections::BTreeMap<String, Value>>();
+        json!({"contract":factor_events::CONTRACT,"results":results,"read_only":true})
+    };
+    println!("{}", serde_json::to_string(&output)?);
+    Ok(())
+}

crates/fidc-core/src/daily_patterns.rs

@@ -1,6 +1,6 @@
 //! Completed-session OHLCV rules shared by research and strategy execution.
 use crate::DataSet;
-use chrono::NaiveDate;
+use chrono::{FixedOffset, NaiveDate, TimeZone};
 use serde::{Deserialize, Serialize};
 use serde_json::{Value, json};
 use std::collections::{BTreeMap, BTreeSet};
@@ -9,6 +9,7 @@ pub const CONTRACT: &str = "fidc_daily_ohlcv_pattern_v1";
 
 pub fn catalog() -> Value {
     json!({"contract":CONTRACT,"templates":{
+        "expression":{"label":"指标与事件条件","parameters":{"history_window":[300,2,3000]},"stages":["selection","buy","sell","position_management"],"method":"冻结历史窗口与表达式；预热不足或未定义值不产生信号。复用共享指标事件内核，不修改既有任务。"},
         "strength":{"label":"趋势强势","parameters":{"momentum_window":[25,5,120],"fast_window":[20,2,60],"slow_window":[60,20,252]},"stages":["selection","buy"],"method":"收盘价>短均线>长均线，按区间动量排序；不是当日金叉。"},
         "breakout":{"label":"前高突破","parameters":{"high_window":[60,5,252],"volume_window":[10,2,60],"volume_multiple":[1.3,1,10],"max_upper_shadow":[0.1,0,1]},"stages":["selection","buy"],"method":"收盘突破此前N日最高价，量达到此前M日均量倍数，上影比例受限；参考窗口不含当日。"},
         "volume_spike":{"label":"放量上涨","parameters":{"volume_window":[5,2,60],"volume_multiple":[3.0,1,10]},"stages":["selection","buy"],"method":"当日上涨且量达到此前N日最大量的指定倍数；不等同价格创新高。"},
@@ -24,9 +25,39 @@ pub struct PatternSpec {
     pub template: String,
     #[serde(default)]
     pub parameters: BTreeMap<String, Value>,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub expression: Option<crate::factor_events::Expr>,
 }
 impl PatternSpec {
     pub fn validate(mut self) -> Result<Self, String> {
+        if (self.template == "expression") != self.expression.is_some() {
+            return Err("expression_template_requires_expression_only".into());
+        }
+        if let Some(expr) = &self.expression {
+            let supported = [
+                "open",
+                "high",
+                "low",
+                "close",
+                "volume",
+                "raw_open",
+                "raw_high",
+                "raw_low",
+                "raw_close",
+                "prev_close",
+                "amount",
+            ];
+            let missing = crate::factor_events::field_dependencies(expr)
+                .into_iter()
+                .filter(|f| !supported.contains(&f.as_str()))
+                .collect::<Vec<_>>();
+            if !missing.is_empty() {
+                return Err(format!(
+                    "expression_source_mapping_required: {}",
+                    missing.join(",")
+                ));
+            }
+        }
         let catalog = catalog();
         let definition = catalog["templates"]
             .get(&self.template)
@@ -66,6 +97,7 @@ impl PatternSpec {
     }
     pub fn history_len(&self) -> usize {
         match self.template.as_str() {
+            "expression" => self.n("history_window"),
             "strength" => self.n("slow_window").max(self.n("momentum_window") + 1),
             "breakout" => self.n("high_window").max(self.n("volume_window")) + 1,
             "volume_spike" | "volume_down" => self.n("volume_window") + 1,
@@ -85,6 +117,10 @@ pub struct PatternBar {
     pub low: Option<f64>,
     pub close: Option<f64>,
     pub volume: Option<f64>,
+    #[serde(default)]
+    pub prev_close: Option<f64>,
+    #[serde(default)]
+    pub amount: Option<f64>,
     pub adjustment_factor_backward1: Option<f64>,
     pub paused: Option<bool>,
     #[serde(default)]
@@ -246,6 +282,111 @@ pub fn evaluate(
     result.anchor = json!({"date":days[len-1],"raw_close":by_day[&days[len-1]].close,"factor":by_day[&days[len-1]].adjustment_factor_backward1});
     let mut score = None;
     match spec.template.as_str() {
+        "expression" => {
+            let zone = FixedOffset::east_opt(8 * 3600).unwrap();
+            let timestamps = days
+                .iter()
+                .map(|d| {
+                    zone.from_local_datetime(&d.and_hms_opt(16, 0, 0).unwrap())
+                        .single()
+                        .unwrap()
+                })
+                .collect::<Vec<_>>();
+            let anchor = by_day[&days[len - 1]].adjustment_factor_backward1.unwrap();
+            let mut fields = BTreeMap::from([
+                (
+                    "open".into(),
+                    prices.iter().map(|b| Some(b.0 / anchor)).collect(),
+                ),
+                (
+                    "high".into(),
+                    prices.iter().map(|b| Some(b.1 / anchor)).collect(),
+                ),
+                (
+                    "low".into(),
+                    prices.iter().map(|b| Some(b.2 / anchor)).collect(),
+                ),
+                (
+                    "close".into(),
+                    prices.iter().map(|b| Some(b.3 / anchor)).collect(),
+                ),
+                ("volume".into(), prices.iter().map(|b| Some(b.4)).collect()),
+            ]);
+            for (name, index) in [
+                ("raw_open", 0),
+                ("raw_high", 1),
+                ("raw_low", 2),
+                ("raw_close", 3),
+            ] {
+                fields.insert(
+                    name.into(),
+                    days.iter()
+                        .map(|d| {
+                            let b = by_day[d];
+                            [b.open, b.high, b.low, b.close][index]
+                        })
+                        .collect(),
+                );
+            }
+            let needed =
+                crate::factor_events::field_dependencies(spec.expression.as_ref().unwrap());
+            for name in ["prev_close", "amount"] {
+                if !needed.contains(name) {
+                    continue;
+                }
+                let values = days
+                    .iter()
+                    .map(|d| {
+                        let b = by_day[d];
+                        let value = number(
+                            if name == "prev_close" {
+                                b.prev_close
+                            } else {
+                                b.amount
+                            },
+                            &series.symbol,
+                            *d,
+                            name,
+                        )?;
+                        if value < 0.0 || (name == "prev_close" && value == 0.0) {
+                            return Err(format!(
+                                "pattern_input_invalid: {} {d} {name}",
+                                series.symbol
+                            ));
+                        }
+                        Ok(Some(value))
+                    })
+                    .collect::<Result<Vec<_>, String>>()?;
+                fields.insert(name.into(), values);
+            }
+            let frame = crate::factor_events::Frame {
+                symbol: series.symbol.clone(),
+                frequency: "1d".into(),
+                decision_at: *timestamps.last().unwrap(),
+                available_at: timestamps.clone(),
+                timestamps,
+                fields,
+            };
+            let values = crate::factor_events::evaluate(spec.expression.as_ref().unwrap(), &frame)?;
+            let latest = values.values.last().copied().flatten();
+            result.values["expression"] = json!(values);
+            result.values["expression_contract"] = json!(crate::factor_events::CONTRACT);
+            result.values["price_policy"] = json!("backward1_anchored_to_decision_close");
+            result.score = latest;
+            if latest.is_none() {
+                result.exclusion = Some(
+                    json!({"reason":"expression_undefined_or_warmup","signal_date":days.last()}),
+                );
+            } else if values.value_type == crate::factor_events::ValueType::Boolean {
+                result.matched = latest == Some(1.0);
+                result
+                    .checks
+                    .push(json!({"label":"组合条件","actual":latest,"operator":"==","threshold":1,"passed":result.matched}));
+            } else {
+                return Err("expression_signal_requires_boolean: 数值因子必须显式比较或组合，不能自动视为买卖信号".into());
+            }
+            return Ok(result);
+        }
         "strength" => {
             let fast = mean(prices[len - spec.n("fast_window")..].iter().map(|b| b.3))?;
             let slow = mean(prices[len - spec.n("slow_window")..].iter().map(|b| b.3))?;
@@ -410,6 +551,8 @@ pub fn evaluate_dataset(
                 low: Some(b.low),
                 close: Some(b.close),
                 volume: Some(b.volume as f64),
+                prev_close: data.factor_numeric_value(d, symbol, "pre_close"),
+                amount: data.factor_numeric_value(d, symbol, "amount"),
                 adjustment_factor_backward1: data
                     .factor(d, symbol)
                     .and_then(|f| f.adjustment_factor_backward1),
@@ -491,10 +634,75 @@ pub fn expression_specs(expression: &str) -> Result<Vec<PatternSpec>, String> {
 #[cfg(test)]
 mod tests {
     use super::*;
+    #[test]
+    fn expression_condition_preserves_native_types_and_rejects_numeric_as_signal() {
+        let make = |expression: Value| {
+            serde_json::from_value::<PatternSpec>(json!({"template":"expression","parameters":{"history_window":3},"expression":expression})).unwrap().validate().unwrap()
+        };
+        let spec = make(
+            json!({"kind":"operator","name":"GT","args":[{"kind":"field","name":"close"},{"kind":"indicator","name":"SMA","inputs":[{"kind":"field","name":"close"}],"parameters":{"optInTimePeriod":2}}]}),
+        );
+        let days = ["2026-09-04", "2026-09-07", "2026-09-08"]
+            .map(|d| NaiveDate::parse_from_str(d, "%Y-%m-%d").unwrap());
+        let series = PatternSeries {
+            symbol: "TEST".into(),
+            name: None,
+            listed_at: None,
+            bars: days
+                .iter()
+                .enumerate()
+                .map(|(i, &date)| {
+                    let p = 10.0 + i as f64;
+                    PatternBar {
+                        date,
+                        open: Some(p),
+                        high: Some(p),
+                        low: Some(p),
+                        close: Some(p),
+                        volume: Some(100.0),
+                        prev_close: Some(p - 1.0),
+                        amount: Some(p * 100.0),
+                        adjustment_factor_backward1: Some(1.0),
+                        paused: Some(false),
+                        source_path: None,
+                    }
+                })
+                .collect(),
+        };
+        let result = evaluate(&spec, &days, &series).unwrap();
+        assert!(result.matched);
+        assert_eq!(result.score, Some(1.0));
+        let vwap_spec = make(
+            json!({"kind":"operator","name":"GT","args":[{"kind":"operator","name":"DIV","args":[{"kind":"field","name":"amount"},{"kind":"field","name":"volume"}]},{"kind":"field","name":"prev_close"}]}),
+        );
+        assert!(evaluate(&vwap_spec, &days, &series).unwrap().matched);
+        let mut missing_amount = series.clone();
+        missing_amount.bars[1].amount = None;
+        assert!(
+            evaluate(&vwap_spec, &days, &missing_amount)
+                .unwrap_err()
+                .contains("amount")
+        );
+        let mut missing_previous=series.clone();missing_previous.bars[1].prev_close=None;
+        assert!(evaluate(&vwap_spec,&days,&missing_previous).unwrap_err().contains("prev_close"));
+        assert!(
+            evaluate(
+                &make(json!({"kind":"field","name":"close"})),
+                &days,
+                &series
+            )
+            .unwrap_err()
+            .contains("requires_boolean")
+        );
+        let mut missing = series.clone();
+        missing.bars[1].close = None;
+        assert!(evaluate(&spec, &days, &missing).is_err());
+    }
     fn fixture(template: &str) -> (PatternSpec, Vec<NaiveDate>, PatternSeries) {
         let spec = PatternSpec {
             template: template.into(),
             parameters: BTreeMap::new(),
+            expression: None,
         }
         .validate()
         .unwrap();
@@ -515,6 +723,8 @@ mod tests {
                     low: Some(c),
                     close: Some(c),
                     volume: Some(1000.0),
+                    prev_close: Some(c - 1.0),
+                    amount: Some(c * 1000.0),
                     adjustment_factor_backward1: Some(1.0),
                     paused: Some(false),
                     source_path: Some("fixture.parquet".into()),

crates/fidc-core/src/factor_cross_section.rs

@@ -0,0 +1,190 @@
+//! Cross-sectional operators require an explicit complete universe, never a UI page.
+use serde::{Deserialize, Serialize};
+use std::collections::{BTreeMap, BTreeSet};
+
+pub const OPERATORS: &[&str] = &[
+    "RANK",
+    "PERCENTILE",
+    "TOP",
+    "BOTTOM",
+    "TOP_PERCENT",
+    "BOTTOM_PERCENT",
+    "WINSORIZE",
+    "INDUSTRY_NEUTRALIZE",
+    "SIZE_NEUTRALIZE",
+];
+
+#[derive(Clone, Debug, Deserialize)]
+#[serde(deny_unknown_fields)]
+pub struct Observation {
+    pub symbol: String,
+    pub value: f64,
+    pub industry: Option<String>,
+    pub market_cap: Option<f64>,
+}
+#[derive(Debug, Serialize)]
+pub struct Output {
+    pub symbol: String,
+    pub value: f64,
+}
+
+fn mean(values: &[f64]) -> f64 {
+    let base = values[0];
+    base + values
+        .iter()
+        .skip(1)
+        .map(|v| (v - base) / values.len() as f64)
+        .sum::<f64>()
+}
+fn quantile(sorted: &[f64], p: f64) -> f64 {
+    let x = p * (sorted.len() - 1) as f64;
+    let l = x.floor() as usize;
+    let r = x.ceil() as usize;
+    sorted[l] + (sorted[r] - sorted[l]) * (x - l as f64)
+}
+
+pub fn evaluate(
+    name: &str,
+    universe: &[String],
+    rows: &[Observation],
+    threshold: f64,
+) -> Result<Vec<Output>, String> {
+    let expected = universe.iter().collect::<BTreeSet<_>>();
+    if rows.is_empty()
+        || rows.len() > 20_000
+        || expected.len() != universe.len()
+        || rows.len() != universe.len()
+        || rows.iter().map(|r| &r.symbol).collect::<BTreeSet<_>>() != expected
+        || rows.iter().any(|r| !r.value.is_finite())
+    {
+        return Err("cross_section_incomplete_or_invalid_universe".into());
+    }
+    if !OPERATORS.contains(&name) || !threshold.is_finite() {
+        return Err("cross_section_operator_invalid".into());
+    }
+    if matches!(name, "TOP" | "BOTTOM") && (threshold < 1.0 || threshold.fract() != 0.0)
+        || matches!(name, "TOP_PERCENT" | "BOTTOM_PERCENT") && !(0.0..=1.0).contains(&threshold)
+        || name == "WINSORIZE" && !(0.0..0.5).contains(&threshold)
+    {
+        return Err("cross_section_threshold_invalid".into());
+    }
+    let mut sorted = rows.iter().map(|r| r.value).collect::<Vec<_>>();
+    sorted.sort_by(f64::total_cmp);
+    let mut industry_values: BTreeMap<&str, Vec<f64>> = BTreeMap::new();
+    if name == "INDUSTRY_NEUTRALIZE" {
+        for row in rows {
+            let industry = row
+                .industry
+                .as_deref()
+                .filter(|v| !v.trim().is_empty())
+                .ok_or("cross_section_pit_industry_missing")?;
+            industry_values.entry(industry).or_default().push(row.value);
+        }
+    }
+    let size = if name == "SIZE_NEUTRALIZE" {
+        let x = rows
+            .iter()
+            .map(|r| {
+                r.market_cap
+                    .filter(|v| v.is_finite() && *v > 0.0)
+                    .map(f64::ln)
+                    .ok_or("cross_section_market_cap_missing")
+            })
+            .collect::<Result<Vec<_>, _>>()?;
+        let xm = mean(&x);
+        let ym = mean(&sorted);
+        let variance = x.iter().map(|v| (v - xm).powi(2)).sum::<f64>();
+        if variance == 0.0 || rows.len() < 3 {
+            return Err("cross_section_size_regression_unidentified".into());
+        }
+        let beta = x
+            .iter()
+            .zip(rows)
+            .map(|(x, y)| (x - xm) * (y.value - ym))
+            .sum::<f64>()
+            / variance;
+        Some((x, xm, ym, beta))
+    } else {
+        None
+    };
+    rows.iter()
+        .enumerate()
+        .map(|(index, row)| {
+            let low = sorted.partition_point(|v| *v < row.value);
+            let high = sorted.partition_point(|v| *v <= row.value);
+            let rank = (low + 1 + high) as f64 / 2.0;
+            let descending = (rows.len() + 1) as f64 - rank;
+            let percentile = if rows.len() == 1 {
+                0.5
+            } else {
+                (rank - 1.0) / (rows.len() - 1) as f64
+            };
+            let value = match name {
+                "RANK" => descending,
+                "PERCENTILE" => percentile,
+                "TOP" => f64::from(descending <= threshold),
+                "BOTTOM" => f64::from(rank <= threshold),
+                "TOP_PERCENT" => f64::from(descending <= threshold * rows.len() as f64),
+                "BOTTOM_PERCENT" => f64::from(rank <= threshold * rows.len() as f64),
+                "WINSORIZE" => row.value.clamp(
+                    quantile(&sorted, threshold),
+                    quantile(&sorted, 1.0 - threshold),
+                ),
+                "INDUSTRY_NEUTRALIZE" => {
+                    row.value - mean(&industry_values[row.industry.as_deref().unwrap()])
+                }
+                "SIZE_NEUTRALIZE" => {
+                    let (x, xm, ym, beta) = size.as_ref().unwrap();
+                    row.value - (ym + beta * (x[index] - xm))
+                }
+                _ => unreachable!(),
+            };
+            if !value.is_finite() {
+                return Err("cross_section_result_nonfinite".into());
+            }
+            Ok(Output {
+                symbol: row.symbol.clone(),
+                value,
+            })
+        })
+        .collect()
+}
+
+#[cfg(test)]
+mod tests {
+    use super::*;
+    fn rows() -> Vec<Observation> {
+        [1.0, 3.0, 3.0, 4.0]
+            .iter()
+            .enumerate()
+            .map(|(i, &value)| Observation {
+                symbol: format!("S{i}"),
+                value,
+                industry: Some(if i < 2 { "A" } else { "B" }.into()),
+                market_cap: Some(10.0 + i as f64),
+            })
+            .collect()
+    }
+    #[test]
+    fn ties_keep_equal_rank_and_missing_universe_rejects() {
+        let r = rows();
+        let u = r.iter().map(|r| r.symbol.clone()).collect::<Vec<_>>();
+        let out = evaluate("RANK", &u, &r, 0.0).unwrap();
+        assert_eq!(
+            out.iter().map(|r| r.value).collect::<Vec<_>>(),
+            vec![4.0, 2.5, 2.5, 1.0]
+        );
+        assert!(evaluate("RANK", &u, &r[..3], 0.0).is_err());
+    }
+    #[test]
+    fn neutralization_preserves_input_order() {
+        let r = rows();
+        let u = r.iter().map(|r| r.symbol.clone()).collect::<Vec<_>>();
+        let out = evaluate("INDUSTRY_NEUTRALIZE", &u, &r, 0.0).unwrap();
+        assert_eq!(
+            out.iter().map(|r| r.value).collect::<Vec<_>>(),
+            vec![-1.0, 1.0, -0.5, 0.5]
+        );
+        assert!(evaluate("TOP_PERCENT", &u, &r, 20.0).is_err());
+    }
+}

crates/fidc-core/src/lib.rs

@@ -3,6 +3,8 @@ pub mod calendar;
 pub mod cost;
 pub mod data;
 pub mod daily_patterns;
+pub mod factor_events;
+pub mod factor_cross_section;
 pub mod engine;
 pub mod event_bus;
 pub mod events;