扩展策略指标因子与滚动函数

crates/fidc-core/src/data.rs

@@ -574,6 +574,18 @@ impl SymbolPriceSeries {
         Some(sum / lookback as f64)
     }
 
+    fn decision_prev_close_values(&self, date: NaiveDate, lookback: usize) -> Option<Vec<f64>> {
+        if lookback == 0 {
+            return None;
+        }
+        let end = self.decision_end_index(date)?;
+        if end < lookback {
+            return None;
+        }
+        let start = end - lookback;
+        Some(self.prev_closes[start..end].to_vec())
+    }
+
     fn decision_volume_moving_average(&self, date: NaiveDate, lookback: usize) -> Option<f64> {
         if lookback == 0 {
             return None;
@@ -587,6 +599,23 @@ impl SymbolPriceSeries {
         Some(sum / lookback as f64)
     }
 
+    fn decision_volume_values(&self, date: NaiveDate, lookback: usize) -> Option<Vec<f64>> {
+        if lookback == 0 {
+            return None;
+        }
+        let end = self.previous_completed_end_index(date)?;
+        if end < lookback {
+            return None;
+        }
+        let start = end - lookback;
+        Some(
+            self.snapshots[start..end]
+                .iter()
+                .map(|snapshot| snapshot.volume as f64)
+                .collect(),
+        )
+    }
+
     fn end_index(&self, date: NaiveDate) -> Option<usize> {
         match self.dates.binary_search(&date) {
             Ok(idx) => Some(idx + 1),
@@ -625,6 +654,7 @@ impl SymbolPriceSeries {
 #[derive(Debug, Clone)]
 struct BenchmarkPriceSeries {
     dates: Vec<NaiveDate>,
+    opens: Vec<f64>,
     closes: Vec<f64>,
     open_prefix: Vec<f64>,
     close_prefix: Vec<f64>,
@@ -641,6 +671,7 @@ impl BenchmarkPriceSeries {
         let close_prefix = prefix_sums(&closes);
         Self {
             dates,
+            opens,
             closes,
             open_prefix,
             close_prefix,
@@ -678,13 +709,20 @@ impl BenchmarkPriceSeries {
     }
 
     fn trailing_values(&self, date: NaiveDate, lookback: usize) -> Vec<f64> {
+        self.trailing_values_for(date, lookback, PriceField::Close)
+    }
+
+    fn trailing_values_for(&self, date: NaiveDate, lookback: usize, field: PriceField) -> Vec<f64> {
         let end = match self.dates.binary_search(&date) {
             Ok(idx) => idx + 1,
             Err(0) => return Vec::new(),
             Err(idx) => idx,
         };
         let start = end.saturating_sub(lookback);
-        self.closes[start..end].to_vec()
+        match field {
+            PriceField::DayOpen | PriceField::Open => self.opens[start..end].to_vec(),
+            PriceField::Close | PriceField::Last => self.closes[start..end].to_vec(),
+        }
     }
 }
 
@@ -944,6 +982,7 @@ impl DataSet {
     ) -> Result<Self, DataSetError> {
         let benchmark_code = collect_benchmark_code(&benchmarks)?;
         let calendar = TradingCalendar::new(benchmarks.iter().map(|item| item.date).collect());
+        let factors = normalize_factor_snapshots(factors);
 
         let instruments = instruments
             .into_iter()
@@ -2009,6 +2048,65 @@ impl DataSet {
         }
     }
 
+    pub fn market_decision_numeric_values(
+        &self,
+        date: NaiveDate,
+        symbol: &str,
+        field: &str,
+        lookback: usize,
+    ) -> Vec<f64> {
+        if lookback == 0 {
+            return Vec::new();
+        }
+        let field = normalize_field(field);
+        match field.as_str() {
+            "close" | "prev_close" | "stock_close" | "price" => self
+                .market_series_by_symbol
+                .get(symbol)
+                .and_then(|series| series.decision_prev_close_values(date, lookback))
+                .unwrap_or_default(),
+            "volume" | "stock_volume" => self
+                .market_series_by_symbol
+                .get(symbol)
+                .and_then(|series| series.decision_volume_values(date, lookback))
+                .unwrap_or_default(),
+            "day_open" | "dayopen" => self
+                .market_series_by_symbol
+                .get(symbol)
+                .map(|series| series.trailing_values(date, lookback, PriceField::DayOpen))
+                .unwrap_or_default(),
+            "open" => self
+                .market_series_by_symbol
+                .get(symbol)
+                .map(|series| series.trailing_values(date, lookback, PriceField::Open))
+                .unwrap_or_default(),
+            "last" | "last_price" => self
+                .market_series_by_symbol
+                .get(symbol)
+                .map(|series| series.trailing_values(date, lookback, PriceField::Last))
+                .unwrap_or_default(),
+            other => self.factor_numeric_values(date, symbol, other, lookback),
+        }
+    }
+
+    pub fn factor_numeric_values(
+        &self,
+        date: NaiveDate,
+        symbol: &str,
+        field: &str,
+        lookback: usize,
+    ) -> Vec<f64> {
+        if lookback == 0 {
+            return Vec::new();
+        }
+        self.calendar
+            .trailing_days(date, lookback)
+            .into_iter()
+            .filter_map(|trading_day| self.factor(trading_day, symbol))
+            .filter_map(|snapshot| factor_numeric_value(snapshot, field))
+            .collect()
+    }
+
     pub fn market_moving_average(
         &self,
         date: NaiveDate,
@@ -2030,6 +2128,21 @@ impl DataSet {
             .moving_average_for(date, lookback, PriceField::Open)
     }
 
+    pub fn benchmark_numeric_values(
+        &self,
+        date: NaiveDate,
+        field: &str,
+        lookback: usize,
+    ) -> Vec<f64> {
+        let field = normalize_field(field);
+        match field.as_str() {
+            "open" | "day_open" | "dayopen" | "benchmark_open" => self
+                .benchmark_series_cache
+                .trailing_values_for(date, lookback, PriceField::Open),
+            _ => self.benchmark_series_cache.trailing_values(date, lookback),
+        }
+    }
+
     pub fn market_open_moving_average(
         &self,
         date: NaiveDate,
@@ -2400,6 +2513,26 @@ fn factor_numeric_value(snapshot: &DailyFactorSnapshot, field: &str) -> Option<f
         "pe_ttm" => Some(snapshot.pe_ttm),
         "turnover_ratio" => snapshot.turnover_ratio,
         "effective_turnover_ratio" => snapshot.effective_turnover_ratio,
+        "ths_market_value_stock" | "ths_market_value_stock_bn" => snapshot
+            .extra_factors
+            .get(field.as_str())
+            .copied()
+            .or(Some(snapshot.market_cap_bn)),
+        "ths_current_mv_stock" | "ths_current_mv_stock_bn" => snapshot
+            .extra_factors
+            .get(field.as_str())
+            .copied()
+            .or(Some(snapshot.free_float_cap_bn)),
+        "ths_turnover_ratio_stock" => snapshot
+            .extra_factors
+            .get(field.as_str())
+            .copied()
+            .or(snapshot.turnover_ratio),
+        "ths_vaild_turnover_stock" | "ths_valid_turnover_stock" => snapshot
+            .extra_factors
+            .get(field.as_str())
+            .copied()
+            .or(snapshot.effective_turnover_ratio),
         other => snapshot.extra_factors.get(other).copied(),
     }
 }
@@ -2509,6 +2642,27 @@ fn normalize_field(field: &str) -> String {
         .to_ascii_lowercase()
 }
 
+fn normalize_factor_snapshots(factors: Vec<DailyFactorSnapshot>) -> Vec<DailyFactorSnapshot> {
+    factors
+        .into_iter()
+        .map(|mut snapshot| {
+            snapshot.extra_factors = snapshot
+                .extra_factors
+                .into_iter()
+                .filter_map(|(field, value)| {
+                    let normalized = normalize_field(&field);
+                    if normalized.is_empty() || !value.is_finite() {
+                        None
+                    } else {
+                        Some((normalized, value))
+                    }
+                })
+                .collect();
+            snapshot
+        })
+        .collect()
+}
+
 fn normalize_history_frequency(frequency: &str) -> Option<String> {
     let normalized = normalize_field(frequency);
     match normalized.as_str() {

crates/fidc-core/src/lib.rs

@@ -9,6 +9,7 @@ pub mod futures;
 pub mod instrument;
 pub mod metrics;
 pub mod platform_expr_strategy;
+pub mod platform_strategy_spec;
 pub mod portfolio;
 pub mod rules;
 pub mod scheduler;
@@ -49,6 +50,15 @@ pub use platform_expr_strategy::{
     PlatformRebalanceSchedule, PlatformScheduleFrequency, PlatformTradeAction,
     PlatformUniverseActionKind,
 };
+pub use platform_strategy_spec::{
+    DynamicRangeConfig, IndexThrottleConfig, MovingAverageFilterConfig, SkipWindowConfig,
+    StrategyBenchmarkSpec, StrategyEngineConfig, StrategyExecutionSpec,
+    StrategyExpressionActionConfig, StrategyExpressionAllocationConfig,
+    StrategyExpressionOrderingConfig, StrategyExpressionRiskConfig,
+    StrategyExpressionScheduleConfig, StrategyExpressionSelectionConfig,
+    StrategyExpressionTradingConfig, StrategyRuntimeEnvironment, StrategyRuntimeExpressions,
+    StrategyRuntimeSpec, platform_expr_config_from_spec, platform_expr_config_from_value,
+};
 pub use portfolio::{CashReceivable, HoldingSummary, PendingCashFlow, PortfolioState, Position};
 pub use rules::{ChinaEquityRuleHooks, EquityRuleHooks, RuleCheck};
 pub use scheduler::{

crates/fidc-core/src/platform_expr_strategy.rs

@@ -641,7 +641,16 @@ impl PlatformExprStrategy {
             "factor"
                 | "day_factor"
                 | "rolling_mean"
+                | "ma"
                 | "sma"
+                | "vma"
+                | "rolling_sum"
+                | "rolling_min"
+                | "rolling_max"
+                | "rolling_stddev"
+                | "stddev"
+                | "rolling_zscore"
+                | "pct_change"
                 | "factor_value"
                 | "get_factor_value"
                 | "factor_text"
@@ -1275,6 +1284,7 @@ impl PlatformExprStrategy {
             .extra_factors
             .get("touched_upper_limit")
             .or_else(|| factor.extra_factors.get("hit_upper_limit"))
+            .or_else(|| factor.extra_factors.get("limit_up_touched"))
             .copied()
             .unwrap_or_default()
             >= 0.5;
@@ -1282,6 +1292,7 @@ impl PlatformExprStrategy {
             .extra_factors
             .get("touched_lower_limit")
             .or_else(|| factor.extra_factors.get("hit_lower_limit"))
+            .or_else(|| factor.extra_factors.get("limit_down_touched"))
             .copied()
             .unwrap_or_default()
             >= 0.5;
@@ -2136,7 +2147,7 @@ impl PlatformExprStrategy {
                 )?);
                 Ok(format!("day_factors[{}]", Self::quote_rhai_string(&key)))
             }
-            "rolling_mean" | "sma" => {
+            "rolling_mean" | "sma" | "ma" => {
                 if args.len() != 2 {
                     return Err(BacktestError::Execution(format!(
                         "{helper} expects 2 arguments"
@@ -2147,6 +2158,60 @@ impl PlatformExprStrategy {
                 let value = self.resolve_rolling_mean(ctx, day, stock, &field, lookback)?;
                 Ok(format!("{value:.12}"))
             }
+            "vma" => {
+                if args.len() != 1 {
+                    return Err(BacktestError::Execution(
+                        "vma expects 1 lookback argument".to_string(),
+                    ));
+                }
+                let lookback = Self::parse_positive_usize(&args[0])?;
+                let value = self.resolve_rolling_mean(ctx, day, stock, "volume", lookback)?;
+                Ok(format!("{value:.12}"))
+            }
+            "rolling_sum" | "rolling_min" | "rolling_max" | "rolling_stddev" | "stddev"
+            | "rolling_zscore" => {
+                if args.len() != 2 {
+                    return Err(BacktestError::Execution(format!(
+                        "{helper} expects field and lookback"
+                    )));
+                }
+                let field = Self::parse_string_or_identifier(&args[0])?;
+                let lookback = Self::parse_positive_usize(&args[1])?;
+                let values = self.resolve_rolling_values(ctx, day, stock, &field, lookback)?;
+                let value = match helper {
+                    "rolling_sum" => values.iter().sum::<f64>(),
+                    "rolling_min" => values.iter().copied().fold(f64::INFINITY, f64::min),
+                    "rolling_max" => values.iter().copied().fold(f64::NEG_INFINITY, f64::max),
+                    "rolling_stddev" | "stddev" => rolling_stddev(&values),
+                    "rolling_zscore" => rolling_zscore(&values),
+                    _ => 0.0,
+                };
+                Ok(Self::format_rhai_float(value))
+            }
+            "pct_change" => {
+                if args.len() != 2 {
+                    return Err(BacktestError::Execution(
+                        "pct_change expects field and lookback".to_string(),
+                    ));
+                }
+                let field = Self::parse_string_or_identifier(&args[0])?;
+                let lookback = Self::parse_positive_usize(&args[1])?;
+                let values = self.resolve_rolling_values(
+                    ctx,
+                    day,
+                    stock,
+                    &field,
+                    lookback.saturating_add(1),
+                )?;
+                let first = values.first().copied().unwrap_or_default();
+                let last = values.last().copied().unwrap_or_default();
+                let value = if first.abs() <= f64::EPSILON {
+                    0.0
+                } else {
+                    last / first - 1.0
+                };
+                Ok(Self::format_rhai_float(value))
+            }
             "factor_value" | "get_factor_value" => {
                 if args.is_empty() || args.len() > 2 {
                     return Err(BacktestError::Execution(format!(
@@ -2639,6 +2704,60 @@ impl PlatformExprStrategy {
         })
     }
 
+    fn resolve_rolling_values(
+        &self,
+        ctx: &StrategyContext<'_>,
+        day: &DayExpressionState,
+        stock: Option<&StockExpressionState>,
+        field: &str,
+        lookback: usize,
+    ) -> Result<Vec<f64>, BacktestError> {
+        if lookback == 0 {
+            return Err(BacktestError::Execution(
+                "rolling lookback must be positive".to_string(),
+            ));
+        }
+        let values = match field {
+            "benchmark_open" => ctx
+                .data
+                .benchmark_numeric_values(day.date, "open", lookback),
+            "benchmark_close" => ctx
+                .data
+                .benchmark_numeric_values(day.date, "close", lookback),
+            "signal_open" => ctx.data.market_decision_numeric_values(
+                day.date,
+                &self.config.signal_symbol,
+                "open",
+                lookback,
+            ),
+            "signal_close" => ctx.data.market_decision_numeric_values(
+                day.date,
+                &self.config.signal_symbol,
+                "close",
+                lookback,
+            ),
+            "signal_volume" => ctx.data.market_decision_numeric_values(
+                day.date,
+                &self.config.signal_symbol,
+                "volume",
+                lookback,
+            ),
+            other => {
+                let stock = stock.ok_or_else(|| {
+                    BacktestError::Execution(format!("rolling_{other} requires stock context"))
+                })?;
+                ctx.data
+                    .market_decision_numeric_values(day.date, &stock.symbol, other, lookback)
+            }
+        };
+        if values.len() < lookback {
+            return Err(BacktestError::Execution(format!(
+                "missing rolling mean for field {field} with lookback {lookback}"
+            )));
+        }
+        Ok(values)
+    }
+
     fn is_missing_rolling_mean_error(error: &BacktestError) -> bool {
         matches!(
             error,
@@ -4514,6 +4633,35 @@ impl Strategy for PlatformExprStrategy {
     }
 }
 
+fn rolling_stddev(values: &[f64]) -> f64 {
+    if values.is_empty() {
+        return 0.0;
+    }
+    let mean = values.iter().sum::<f64>() / values.len() as f64;
+    let variance = values
+        .iter()
+        .map(|value| {
+            let diff = value - mean;
+            diff * diff
+        })
+        .sum::<f64>()
+        / values.len() as f64;
+    variance.sqrt()
+}
+
+fn rolling_zscore(values: &[f64]) -> f64 {
+    let Some(latest) = values.last().copied() else {
+        return 0.0;
+    };
+    let mean = values.iter().sum::<f64>() / values.len() as f64;
+    let stddev = rolling_stddev(values);
+    if stddev <= f64::EPSILON {
+        0.0
+    } else {
+        (latest - mean) / stddev
+    }
+}
+
 #[cfg(test)]
 mod tests {
     use std::collections::{BTreeMap, BTreeSet};
@@ -5248,6 +5396,144 @@ mod tests {
         );
     }
 
+    #[test]
+    fn platform_helpers_support_generic_rolling_stats_and_normalized_factors() {
+        let dates = [d(2025, 1, 2), d(2025, 1, 3), d(2025, 1, 6)];
+        let market_rows = dates
+            .iter()
+            .enumerate()
+            .map(|(index, date)| DailyMarketSnapshot {
+                date: *date,
+                symbol: "000001.SZ".to_string(),
+                timestamp: None,
+                day_open: 10.0 + index as f64,
+                open: 10.0 + index as f64,
+                high: 10.5 + index as f64,
+                low: 9.5 + index as f64,
+                close: 10.2 + index as f64,
+                last_price: 10.2 + index as f64,
+                bid1: 10.2 + index as f64,
+                ask1: 10.2 + index as f64,
+                prev_close: 10.0 + index as f64,
+                volume: 100 + index as u64 * 100,
+                tick_volume: 0,
+                bid1_volume: 0,
+                ask1_volume: 0,
+                trading_phase: None,
+                paused: false,
+                upper_limit: 20.0,
+                lower_limit: 5.0,
+                price_tick: 0.01,
+            })
+            .collect::<Vec<_>>();
+        let factor_rows = dates
+            .iter()
+            .enumerate()
+            .map(|(index, date)| DailyFactorSnapshot {
+                date: *date,
+                symbol: "000001.SZ".to_string(),
+                market_cap_bn: 12.0,
+                free_float_cap_bn: 10.0,
+                pe_ttm: 8.0,
+                turnover_ratio: Some(1.0),
+                effective_turnover_ratio: Some(1.0),
+                extra_factors: BTreeMap::from([("Mixed_Factor".to_string(), index as f64 + 5.0)]),
+            })
+            .collect::<Vec<_>>();
+        let candidate_rows = dates
+            .iter()
+            .map(|date| CandidateEligibility {
+                date: *date,
+                symbol: "000001.SZ".to_string(),
+                is_st: false,
+                is_new_listing: false,
+                is_paused: false,
+                allow_buy: true,
+                allow_sell: true,
+                is_kcb: false,
+                is_one_yuan: false,
+            })
+            .collect::<Vec<_>>();
+        let benchmark_rows = dates
+            .iter()
+            .map(|date| BenchmarkSnapshot {
+                date: *date,
+                benchmark: "000852.SH".to_string(),
+                open: 1000.0,
+                close: 1002.0,
+                prev_close: 998.0,
+                volume: 1_000_000,
+            })
+            .collect::<Vec<_>>();
+        let data = DataSet::from_components(
+            vec![Instrument {
+                symbol: "000001.SZ".to_string(),
+                name: "Ping An Bank".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,
+            factor_rows,
+            candidate_rows,
+            benchmark_rows,
+        )
+        .expect("dataset");
+        let portfolio = PortfolioState::new(1_000_000.0);
+        let subscriptions = BTreeSet::new();
+        let ctx = StrategyContext {
+            execution_date: dates[2],
+            decision_date: dates[2],
+            decision_index: 2,
+            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 = "000001.SZ".to_string();
+        cfg.rotation_enabled = false;
+        cfg.benchmark_short_ma_days = 1;
+        cfg.benchmark_long_ma_days = 1;
+        cfg.explicit_actions = vec![PlatformTradeAction::Order {
+            kind: PlatformExplicitOrderKind::Value,
+            symbol: "000001.SZ".to_string(),
+            amount_expr: "1000".to_string(),
+            limit_price_expr: None,
+            start_time_expr: None,
+            end_time_expr: None,
+            when_expr: Some(
+                concat!(
+                    "ma(\"close\", 2) == 11.5",
+                    " && vma(2) == 150.0",
+                    " && rolling_sum(\"volume\", 2) == 300.0",
+                    " && rolling_min(\"close\", 2) == 11.0",
+                    " && rolling_max(\"close\", 2) == 12.0",
+                    " && stddev(\"close\", 2) > 0.49",
+                    " && rolling_zscore(\"close\", 2) > 0.9",
+                    " && pct_change(\"close\", 1) > 0.09",
+                    " && factor_value(\"mixed_factor\") == 7.0"
+                )
+                .to_string(),
+            ),
+            reason: "rolling_stats_entry".to_string(),
+        }];
+        let mut strategy = PlatformExprStrategy::new(cfg);
+
+        let decision = strategy.on_day(&ctx).expect("platform decision");
+
+        assert_eq!(decision.order_intents.len(), 1);
+    }
+
     #[test]
     fn platform_strategy_emits_target_shares_explicit_action() {
         let date = d(2025, 2, 3);

crates/fidc-core/src/strategy.rs

@@ -1675,6 +1675,7 @@ impl OmniMicroCapStrategy {
             .max(1)
     }
 
+    #[allow(dead_code)]
     fn projected_buy_quantity(&self, cash: f64, sizing_price: f64, execution_price: f64) -> u32 {
         if cash <= 0.0 || sizing_price <= 0.0 || execution_price <= 0.0 {
             return 0;
@@ -1856,6 +1857,7 @@ impl OmniMicroCapStrategy {
         Some(fill.quantity)
     }
 
+    #[allow(dead_code)]
     fn projected_market_fillable_quantity(
         &self,
         ctx: &StrategyContext<'_>,

crates/fidc-core/src/strategy_ai.rs

@@ -97,10 +97,10 @@ pub fn built_in_strategy_manual() -> StrategyAiManual {
             "平台策略脚本采用声明式 DSL + 表达式执行模型。".to_string(),
             "支持 let 变量、fn 自定义函数、when/unless/else 条件块、可用指标/因子字段映射。".to_string(),
             "支持数值型和字符串型因子，字符串字段可用于行业、概念、标签、板块等分类过滤。".to_string(),
-            "当前默认回测数据已支持 OHLCV、市值、流通市值、换手率、有效换手率、上市天数、停牌/ST/板块、涨跌停价格、tick 触达涨跌停、常用价格/成交量均线；复杂技术指标和财务报表字段必须来自预计算因子或后续扩展函数。".to_string(),
+            "当前默认回测数据已支持 OHLCV、市值、流通市值、换手率、有效换手率、上市天数、停牌/ST/板块、涨跌停价格、tick 触达涨跌停、常用价格/成交量均线，以及 stock_indicator_factors_v1 中已入库的通用指标因子。".to_string(),
             "AI 生成策略时只能输出完整 engine-script 代码，不输出 Markdown、解释、推理过程、JSON 包装或手册复述。".to_string(),
             "表达式字段以运行时字段为准：市值使用 market_cap，流通市值使用 free_float_cap；不要在策略表达式中使用数据库原始字段 float_market_cap。".to_string(),
-            "60日价格均线使用 rolling_mean(\"close\", 60)，不要使用 ma60、stock_ma60、signal_ma60 或 benchmark_ma60。".to_string(),
+            "任意窗口价格均线使用 rolling_mean(\"close\", n) 或 ma(\"close\", n)，任意窗口均量使用 rolling_mean(\"volume\", n) 或 vma(n)；不要使用未列出的 ma60、stock_ma60、signal_ma60 或 benchmark_ma60 变量。".to_string(),
             "自定义 fn 必须通过参数传入运行时字段；不要用 fn score() 这类零参数函数直接引用 market_cap、close、ma5 等股票字段。".to_string(),
             "禁止自由 Python/JavaScript 命令式语句，最终必须输出平台 DSL。".to_string(),
         ],
@@ -323,9 +323,11 @@ pub fn built_in_strategy_manual() -> StrategyAiManual {
             ManualFunction { name: "order/order_status/order_avg_price/order_transaction_cost".to_string(), signature: "ctx.order(order_id)".to_string(), detail: "按订单 id 查询运行时订单对象，支持已结束订单和当前挂单。返回字段包括 status、filled_quantity、unfilled_quantity、avg_price、transaction_cost、symbol、side、reason；可用便捷函数读取状态、成交均价和费用，对齐 平台内核 Order 的核心属性。".to_string() },
             ManualFunction { name: "account/portfolio_view/accounts".to_string(), signature: "ctx.account()".to_string(), detail: "返回当前股票账户/组合运行时视图，字段包括 account_type、cash、available_cash、frozen_cash、market_value、total_value、unit_net_value、daily_pnl、daily_returns、total_returns、transaction_cost、trading_pnl、position_pnl 等；DSL 中同名字段可直接使用。也可用 ctx.stock_account()、ctx.account_by_type(\"STOCK\")、ctx.accounts() 按账户类型读取；当前股票回测路径不会把 FUTURE 虚假映射成 STOCK。".to_string() },
             ManualFunction { name: "deposit_withdraw/finance_repay/management_fee".to_string(), signature: "account.deposit_withdraw(amount, receiving_days=0)".to_string(), detail: "策略账户资金动作。deposit_withdraw 正数入金、负数出金；receiving_days 大于 0 时按交易日延迟到账，并保持净值口径不把外部资金流当成收益。finance_repay 正数融资、负数还款，会同步维护 cash_liabilities。set_management_fee_rate 设置结算管理费率；普通策略可覆盖 management_fee(ctx, rate) 自定义计算器，对齐 平台内核 管理费回调能力。".to_string() },
-            ManualFunction { name: "rolling_mean".to_string(), signature: "rolling_mean(\"field\", lookback)".to_string(), detail: "任意字段滚动均值，支持 volume/amount/turnover_ratio、signal_open/signal_close、benchmark_open/benchmark_close 等。个股 volume 与 close 均按当前交易日前已完成交易日计算；单只股票历史窗口不足时，在选股过滤和买入仓位表达式中按不通过/0 仓处理，不会中断整次回测。任意成交量窗口推荐用它，比如 rolling_mean(\"volume\", 15)。".to_string() },
+            ManualFunction { name: "rolling_mean / sma / ma".to_string(), signature: "rolling_mean(\"field\", lookback) / ma(\"close\", 20)".to_string(), detail: "任意字段滚动均值，支持 close、volume、amount、turnover_ratio、effective_turnover_ratio、signal_open/signal_close、benchmark_open/benchmark_close 和所有数值型 extra_factors。个股 close 使用当前交易日前已完成收盘序列，volume 使用当前交易日前已完成成交量序列；历史窗口不足时在选股过滤和买入仓位表达式中按不通过/0 仓处理。".to_string() },
-            ManualFunction { name: "sma".to_string(), signature: "sma(\"field\", lookback)".to_string(), detail: "rolling_mean 的别名。任意价格均线窗口推荐用它，比如 sma(\"close\", 15)。".to_string() },
+            ManualFunction { name: "vma".to_string(), signature: "vma(60)".to_string(), detail: "rolling_mean(\"volume\", lookback) 的便捷别名，用于任意窗口成交量均线，例如 vma(5) < vma(60)。".to_string() },
-            ManualFunction { name: "复杂技术指标".to_string(), signature: "factor_value(\"macd\", 1) 或预计算字段".to_string(), detail: "BOLL、EMA、WMA、DEMA、TEMA、KAMA、SAR、ADX、CCI、MACD、RSI、KDJ、WILLR、ATR、ROC、TRIX、MFI、Aroon、OBV、ADL、Beta、相关系数、线性回归、标准差、方差、K 线形态等目前不是默认内建函数；可先预计算成数值因子，再用 factor_value/rolling_mean 读取。".to_string() },
+            ManualFunction { name: "rolling_sum / rolling_min / rolling_max".to_string(), signature: "rolling_sum(\"volume\", 20)".to_string(), detail: "任意数值字段滚动求和、最小值、最大值。可用于量能收缩、区间高低点、资金活跃度等过滤或排序。".to_string() },
+            ManualFunction { name: "rolling_stddev / stddev / rolling_zscore / pct_change".to_string(), signature: "stddev(\"close\", 20) / pct_change(\"close\", 10)".to_string(), detail: "滚动标准差、最新值 Z 分数和区间涨跌幅。pct_change(field, n) 会读取 n+1 个窗口点并计算 latest / first - 1。".to_string() },
+            ManualFunction { name: "数据库指标因子".to_string(), signature: "factor_value(\"ths_valid_turnover_stock\", 1)".to_string(), detail: "stock_indicator_factors_v1 中的指标会进入 extra_factors，可用 factor(\"字段\")、factors[\"字段\"]、factor_value(\"字段\", lookback) 或 rolling_mean(\"字段\", n) 读取。市值类指标统一提供亿元口径别名 ths_market_value_stock、ths_market_value_stock_bn、ths_current_mv_stock、ths_current_mv_stock_bn，同时保留 raw 后缀原始值。".to_string() },
             ManualFunction { name: "round/floor/ceil/abs/min/max/clamp".to_string(), signature: "round(x)".to_string(), detail: "常用数值函数。".to_string() },
             ManualFunction { name: "safe_div".to_string(), signature: "safe_div(lhs, rhs, fallback)".to_string(), detail: "安全除法。".to_string() },
             ManualFunction { name: "contains/starts_with/ends_with/lower/upper/trim/strlen".to_string(), signature: "starts_with(symbol, \"60\")".to_string(), detail: "字符串辅助函数。".to_string() },
@@ -343,7 +345,7 @@ pub fn built_in_strategy_manual() -> StrategyAiManual {
             },
             ManualFactorSource {
                 table: "扩展指标因子".to_string(),
-                detail: "当前可用扩展指标主要包括总市值、流通市值、换手率、有效换手率；其他财务、行业、概念、陆股通、技术指标等只有落地为可用因子后才可在策略中直接使用。".to_string(),
+                detail: "来自 stock_indicator_factors_v1 和运行时 extra_factors。已入库指标会自动进入策略运行时，字段名使用 dataset 小写下划线；市值类默认换算为亿元口径，raw 后缀保留原始 indicator_value。".to_string(),
                 fields: vec![],
             },
             ManualFactorSource {
@@ -428,7 +430,7 @@ pub fn render_manual_markdown(manual: &StrategyAiManual) -> String {
     out.push_str("- 只使用支持语句块：`market`、`benchmark`、`signal`、`rebalance.every_days(...).at([...])`、`selection.limit`、`selection.market_cap_band`、`filter.stock_ma`、`filter.stock_expr`、`ordering.rank_by`、`ordering.rank_expr`、`allocation.buy_scale`、`risk.stop_loss`、`risk.take_profit`、`risk.index_exposure`、`execution.matching_type`、`execution.slippage`、`universe.exclude`。\n");
     out.push_str("- 禁止伪 DSL：`filter(...)`、`rank(...)`、`select.top(...)`、`weight.equal(...)`、`sell_rule(...)`、`backtest(...)`、`risk.max_position(...)`。\n");
     out.push_str("- 市值表达式字段只能用 `market_cap` 或 `free_float_cap`；不要使用数据库原始字段 `float_market_cap`。\n");
-    out.push_str("- 60日价格均线使用 `rolling_mean(\"close\", 60)`；不要使用 `ma60`、`stock_ma60`、`signal_ma60` 或 `benchmark_ma60`。\n");
+    out.push_str("- 任意窗口价格均线使用 `rolling_mean(\"close\", n)` 或 `ma(\"close\", n)`；任意窗口均量使用 `rolling_mean(\"volume\", n)` 或 `vma(n)`；不要使用未列出的 `ma60`、`stock_ma60`、`signal_ma60` 或 `benchmark_ma60` 变量。\n");
     out.push_str("- 自定义 `fn` 必须通过参数传入运行时字段；不要用 `fn score()` 这类零参数函数直接引用 `market_cap`、`close`、`ma5` 等股票字段。\n");
     out.push_str("- `selection.market_cap_band` 必须写命名参数：`field=\"market_cap\"` 或 `field=\"free_float_cap\"`，并包含 `lower=...` 与 `upper=...`。\n");
     out.push_str("- `risk.index_exposure(...)` 只能传一个表达式；`execution.matching_type(...)` 和 `execution.slippage(...)` 必须使用手册列出的合法取值。\n\n");
@@ -506,7 +508,7 @@ pub fn build_generation_prompt(
     prompt.push_str("- 生成的代码必须能转换为 strategy_spec 并提交 POST /v1/backtests。\n");
     prompt.push_str("- 不要使用手册未列出的字段、函数或外部平台 API 名称。\n\n");
     prompt.push_str("只允许使用这些可编译语句：market、benchmark、signal、rebalance.every_days(...).at([...])、selection.limit、selection.market_cap_band、filter.stock_ma、filter.stock_expr、ordering.rank_by、ordering.rank_expr、allocation.buy_scale、risk.stop_loss、risk.take_profit、risk.index_exposure、execution.matching_type、execution.slippage、universe.exclude。禁止输出 filter(...)、rank(...)、select.top(...)、weight.equal()、sell_rule(...)、backtest(...)、risk.max_position(...) 这类未支持伪语法。\n");
-    prompt.push_str("参数形态必须严格：selection.market_cap_band 必须写 field=\"market_cap\" 或 field=\"free_float_cap\", lower=..., upper=...；禁止使用 float_market_cap；禁止使用 ma60、stock_ma60、signal_ma60、benchmark_ma60，60日价格均线写 rolling_mean(\"close\", 60)；不要生成 fn score() 这类零参数函数，股票字段排序直接写在 ordering.rank_expr 内或用带参数函数；布尔字段按布尔使用，写 !is_st、!paused、!at_upper_limit、!at_lower_limit，不要写 is_st == 0；risk.index_exposure 只能传一个数值表达式，例如 ((signal_close < signal_ma20) ? 0.35 : 1.0)；execution.matching_type 只能取 next_tick_last、next_tick_best_own、next_tick_best_counterparty、counterparty_offer、vwap、current_bar_close、next_bar_open、open_auction；execution.slippage 必须写 execution.slippage(\"none\") 或 execution.slippage(\"price_ratio\", 0.001)。\n");
+    prompt.push_str("参数形态必须严格：selection.market_cap_band 必须写 field=\"market_cap\" 或 field=\"free_float_cap\", lower=..., upper=...；禁止使用 float_market_cap；禁止使用 ma60、stock_ma60、signal_ma60、benchmark_ma60，60日价格均线写 rolling_mean(\"close\", 60) 或 ma(\"close\", 60)，任意窗口均量写 rolling_mean(\"volume\", n) 或 vma(n)；不要生成 fn score() 这类零参数函数，股票字段排序直接写在 ordering.rank_expr 内或用带参数函数；布尔字段按布尔使用，写 !is_st、!paused、!at_upper_limit、!at_lower_limit，不要写 is_st == 0；risk.index_exposure 只能传一个数值表达式，例如 ((signal_close < signal_ma20) ? 0.35 : 1.0)；execution.matching_type 只能取 next_tick_last、next_tick_best_own、next_tick_best_counterparty、counterparty_offer、vwap、current_bar_close、next_bar_open、open_auction；execution.slippage 必须写 execution.slippage(\"none\") 或 execution.slippage(\"price_ratio\", 0.001)。\n");
     prompt.push_str("回测成功但 tradeCount=0 或 holdingCount=0 是无效策略；第一版必须保持稳定买入覆盖率，复杂因子只能在后续优化中逐步加严。\n");
     prompt.push_str("可参考但不要照抄的最小模板，回复时不要包含 ``` 代码围栏：\nstrategy(\"cn_a_smallcap_factor_rotation\") {\nmarket(\"CN_A\")\nbenchmark(\"000852.SH\")\nsignal(\"000001.SH\")\nrebalance.every_days(5).at([\"10:18\"])\nselection.limit(40)\nselection.market_cap_band(field=\"market_cap\", lower=0, upper=1000)\nfilter.stock_expr(listed_days >= 60 && !is_st && !paused && close > 2 && !at_upper_limit && !at_lower_limit)\nordering.rank_by(\"market_cap\", \"asc\")\nallocation.buy_scale(1.0)\nrisk.index_exposure((signal_close < signal_ma20) ? 0.35 : 1.0)\nrisk.stop_loss(holding_return < -0.08)\nexecution.slippage(\"price_ratio\", 0.001)\n}\n\n");
     prompt.push_str("用户目标：\n");
@@ -535,6 +537,7 @@ pub fn build_optimization_prompt(
     prompt.push_str("输出格式硬约束：回复第一行必须是 strategy(\"...\")、let、fn、const 或 //；回复中不得包含 Markdown、解释、思考过程、手册复述、JSON 包装或自然语言总结。\n");
     prompt.push_str("长度硬约束：策略代码目标 80 行以内，只保留必要 let/fn/strategy 块；不要复制下面的手册片段、历史策略全文或字段清单。\n");
     prompt.push_str("只修改与优化目标相关的少量参数或过滤条件，保留原策略的市场、基准、信号指数和核心风控；不要引入手册未列出的字段或外部平台 API 名称。\n");
+    prompt.push_str("优化可以调整调仓周期、持仓数、市值带、filter.stock_expr、ordering.rank_expr、allocation.buy_scale、止盈止损；如上一轮无交易或质量分过低，必须先放宽过滤条件并优先使用已入库指标因子、rolling_mean/ma/vma/rolling_stddev/pct_change 等支持函数。\n");
     prompt.push_str("优化目标：\n");
     prompt.push_str(&format!("- {}\n\n", request.objective));
     prompt.push_str("当前策略代码如下，仅作为输入参考；回复时不要包含 Markdown 代码围栏：\n");