diff --git a/crates/fidc-core/src/data.rs b/crates/fidc-core/src/data.rs
index c3c409b..777f789 100644
--- a/crates/fidc-core/src/data.rs
+++ b/crates/fidc-core/src/data.rs
@@ -451,7 +451,6 @@ struct SymbolPriceSeries {
     closes: Vec<f64>,
     prev_closes: Vec<f64>,
     last_prices: Vec<f64>,
-    volumes: Vec<f64>,
     open_prefix: Vec<f64>,
     close_prefix: Vec<f64>,
     prev_close_prefix: Vec<f64>,
@@ -485,7 +484,6 @@ impl SymbolPriceSeries {
             closes,
             prev_closes,
             last_prices,
-            volumes,
             open_prefix,
             close_prefix,
             prev_close_prefix,
@@ -532,6 +530,14 @@ impl SymbolPriceSeries {
         }
     }
 
+    fn previous_completed_end_index(&self, date: NaiveDate) -> Option<usize> {
+        match self.dates.binary_search(&date) {
+            Ok(idx) => Some(idx),
+            Err(0) => None,
+            Err(idx) => Some(idx),
+        }
+    }
+
     fn decision_close_moving_average(&self, date: NaiveDate, lookback: usize) -> Option<f64> {
         if lookback == 0 {
             return None;
@@ -545,28 +551,11 @@ impl SymbolPriceSeries {
         Some(sum / lookback as f64)
     }
 
-    fn decision_close_rolling_average(&self, date: NaiveDate, lookback: usize) -> Option<f64> {
-        if lookback == 0 {
-            return None;
-        }
-        let end = self.decision_end_index(date)?;
-        if end == 0 {
-            return None;
-        }
-        let start = end.saturating_sub(lookback);
-        let count = end.saturating_sub(start);
-        if count == 0 {
-            return None;
-        }
-        let sum = self.prev_close_prefix[end] - self.prev_close_prefix[start];
-        Some(sum / count as f64)
-    }
-
     fn decision_volume_moving_average(&self, date: NaiveDate, lookback: usize) -> Option<f64> {
         if lookback == 0 {
             return None;
         }
-        let end = self.decision_end_index(date)?;
+        let end = self.previous_completed_end_index(date)?;
         if end < lookback {
             return None;
         }
@@ -575,23 +564,6 @@ impl SymbolPriceSeries {
         Some(sum / lookback as f64)
     }
 
-    fn decision_volume_rolling_average(&self, date: NaiveDate, lookback: usize) -> Option<f64> {
-        if lookback == 0 {
-            return None;
-        }
-        let end = self.decision_end_index(date)?;
-        if end == 0 {
-            return None;
-        }
-        let start = end.saturating_sub(lookback);
-        let count = end.saturating_sub(start);
-        if count == 0 {
-            return None;
-        }
-        let sum = self.volume_prefix[end] - self.volume_prefix[start];
-        Some(sum / count as f64)
-    }
-
     fn end_index(&self, date: NaiveDate) -> Option<usize> {
         match self.dates.binary_search(&date) {
             Ok(idx) => Some(idx + 1),
@@ -630,7 +602,6 @@ impl SymbolPriceSeries {
 #[derive(Debug, Clone)]
 struct BenchmarkPriceSeries {
     dates: Vec<NaiveDate>,
-    opens: Vec<f64>,
     closes: Vec<f64>,
     open_prefix: Vec<f64>,
     close_prefix: Vec<f64>,
@@ -647,7 +618,6 @@ impl BenchmarkPriceSeries {
         let close_prefix = prefix_sums(&closes);
         Self {
             dates,
-            opens,
             closes,
             open_prefix,
             close_prefix,
@@ -2014,11 +1984,11 @@ impl DataSet {
             "close" | "prev_close" | "stock_close" | "price" => self
                 .market_series_by_symbol
                 .get(symbol)
-                .and_then(|series| series.decision_close_rolling_average(date, lookback)),
+                .and_then(|series| series.decision_close_moving_average(date, lookback)),
             "volume" | "stock_volume" => self
                 .market_series_by_symbol
                 .get(symbol)
-                .and_then(|series| series.decision_volume_rolling_average(date, lookback)),
+                .and_then(|series| series.decision_volume_moving_average(date, lookback)),
             "day_open" | "dayopen" => {
                 self.market_moving_average(date, symbol, lookback, PriceField::DayOpen)
             }
@@ -3111,6 +3081,63 @@ mod tests {
         std::env::temp_dir().join(format!("{}_{}_{}.csv", name, std::process::id(), nanos))
     }
 
+    fn market_row(date: &str, prev_close: f64, volume: u64) -> DailyMarketSnapshot {
+        DailyMarketSnapshot {
+            date: NaiveDate::parse_from_str(date, "%Y-%m-%d").unwrap(),
+            symbol: "000001.SZ".to_string(),
+            timestamp: None,
+            day_open: prev_close,
+            open: prev_close,
+            high: prev_close,
+            low: prev_close,
+            close: prev_close,
+            last_price: prev_close,
+            bid1: prev_close,
+            ask1: prev_close,
+            prev_close,
+            volume,
+            tick_volume: 0,
+            bid1_volume: 0,
+            ask1_volume: 0,
+            trading_phase: None,
+            paused: false,
+            upper_limit: prev_close * 1.1,
+            lower_limit: prev_close * 0.9,
+            price_tick: 0.01,
+        }
+    }
+
+    #[test]
+    fn decision_volume_average_uses_previous_completed_days_only() {
+        let series = SymbolPriceSeries::new(&[
+            market_row("2025-01-02", 10.0, 100),
+            market_row("2025-01-03", 11.0, 200),
+            market_row("2025-01-06", 12.0, 10_000),
+        ]);
+
+        assert_eq!(
+            series.decision_close_moving_average(
+                NaiveDate::parse_from_str("2025-01-06", "%Y-%m-%d").unwrap(),
+                2
+            ),
+            Some(11.5)
+        );
+        assert_eq!(
+            series.decision_volume_moving_average(
+                NaiveDate::parse_from_str("2025-01-06", "%Y-%m-%d").unwrap(),
+                2
+            ),
+            Some(150.0)
+        );
+        assert_eq!(
+            series.decision_volume_moving_average(
+                NaiveDate::parse_from_str("2025-01-06", "%Y-%m-%d").unwrap(),
+                3
+            ),
+            None
+        );
+    }
+
     #[test]
     fn reads_mixed_numeric_and_text_extra_factors_from_quoted_csv_json() {
         let path = temp_csv_path("mixed_factor_maps");
diff --git a/crates/fidc-core/src/platform_expr_strategy.rs b/crates/fidc-core/src/platform_expr_strategy.rs
index 9fb4eda..adf08a2 100644
--- a/crates/fidc-core/src/platform_expr_strategy.rs
+++ b/crates/fidc-core/src/platform_expr_strategy.rs
@@ -1224,47 +1224,47 @@ impl PlatformExprStrategy {
         let stock_ma_short = ctx
             .data
             .market_decision_close_moving_average(date, symbol, self.config.stock_short_ma_days)
-            .unwrap_or(0.0);
+            .unwrap_or(f64::NAN);
         let stock_ma_mid = ctx
             .data
             .market_decision_close_moving_average(date, symbol, self.config.stock_mid_ma_days)
-            .unwrap_or(0.0);
+            .unwrap_or(f64::NAN);
         let stock_ma_long = ctx
             .data
             .market_decision_close_moving_average(date, symbol, self.config.stock_long_ma_days)
-            .unwrap_or(0.0);
+            .unwrap_or(f64::NAN);
         let stock_ma5 = ctx
             .data
             .market_decision_close_moving_average(date, symbol, 5)
-            .unwrap_or(stock_ma_short);
+            .unwrap_or(f64::NAN);
         let stock_ma10 = ctx
             .data
             .market_decision_close_moving_average(date, symbol, 10)
-            .unwrap_or(stock_ma_mid);
+            .unwrap_or(f64::NAN);
         let stock_ma20 = ctx
             .data
             .market_decision_close_moving_average(date, symbol, 20)
-            .unwrap_or(stock_ma_long);
+            .unwrap_or(f64::NAN);
         let stock_ma30 = ctx
             .data
             .market_decision_close_moving_average(date, symbol, 30)
-            .unwrap_or(stock_ma20);
+            .unwrap_or(f64::NAN);
         let stock_volume_ma5 = ctx
             .data
             .market_decision_volume_moving_average(date, symbol, 5)
-            .unwrap_or(market.volume as f64);
+            .unwrap_or(f64::NAN);
         let stock_volume_ma10 = ctx
             .data
             .market_decision_volume_moving_average(date, symbol, 10)
-            .unwrap_or(stock_volume_ma5);
+            .unwrap_or(f64::NAN);
         let stock_volume_ma20 = ctx
             .data
             .market_decision_volume_moving_average(date, symbol, 20)
-            .unwrap_or(stock_volume_ma10);
+            .unwrap_or(f64::NAN);
         let stock_volume_ma60 = ctx
             .data
             .market_decision_volume_moving_average(date, symbol, 60)
-            .unwrap_or(stock_volume_ma20);
+            .unwrap_or(f64::NAN);
         let touched_upper_limit = factor
             .extra_factors
             .get("touched_upper_limit")
@@ -2623,6 +2623,14 @@ impl PlatformExprStrategy {
         })
     }
 
+    fn is_missing_rolling_mean_error(error: &BacktestError) -> bool {
+        matches!(
+            error,
+            BacktestError::Execution(message)
+                if message.starts_with("missing rolling mean for field ")
+        )
+    }
+
     fn split_top_level_args(args: &str) -> Vec<String> {
         let mut parts = Vec::new();
         let mut start = 0usize;
@@ -2961,8 +2969,11 @@ impl PlatformExprStrategy {
         if self.config.buy_scale_expr.trim().is_empty() {
             return Ok(1.0);
         }
-        self.eval_float(ctx, &self.config.buy_scale_expr, day, Some(stock), None)
-            .map(|value| value.clamp(0.0, 1.0))
+        match self.eval_float(ctx, &self.config.buy_scale_expr, day, Some(stock), None) {
+            Ok(value) => Ok(value.clamp(0.0, 1.0)),
+            Err(error) if Self::is_missing_rolling_mean_error(&error) => Ok(0.0),
+            Err(error) => Err(error),
+        }
     }
 
     fn eval_i32(
@@ -3832,7 +3843,11 @@ impl PlatformExprStrategy {
         if self.config.stock_filter_expr.trim().is_empty() {
             return Ok(true);
         }
-        self.eval_bool(ctx, &self.config.stock_filter_expr, day, Some(stock), None)
+        match self.eval_bool(ctx, &self.config.stock_filter_expr, day, Some(stock), None) {
+            Ok(value) => Ok(value),
+            Err(error) if Self::is_missing_rolling_mean_error(&error) => Ok(false),
+            Err(error) => Err(error),
+        }
     }
 
     fn field_value(&self, row: &EligibleUniverseSnapshot) -> f64 {
@@ -3919,7 +3934,11 @@ impl PlatformExprStrategy {
         stock: &StockExpressionState,
     ) -> Result<f64, BacktestError> {
         if !self.config.rank_expr.trim().is_empty() {
-            return self.eval_float(ctx, &self.config.rank_expr, day, Some(stock), None);
+            return match self.eval_float(ctx, &self.config.rank_expr, day, Some(stock), None) {
+                Ok(value) => Ok(value),
+                Err(error) if Self::is_missing_rolling_mean_error(&error) => Ok(f64::NAN),
+                Err(error) => Err(error),
+            };
         }
         Ok(self
             .stock_numeric_field_value(candidate, stock, self.config.rank_by.as_str())
@@ -4003,10 +4022,28 @@ impl PlatformExprStrategy {
         for candidate in universe {
             let stock = self.stock_state(ctx, date, &candidate.symbol)?;
             let field_value = self.selection_field_value(&candidate, &stock);
+            if !field_value.is_finite() {
+                if diagnostics.len() < 12 {
+                    diagnostics.push(format!(
+                        "{} rejected by missing selection field",
+                        candidate.symbol
+                    ));
+                }
+                continue;
+            }
             if field_value < band_low || field_value > band_high {
                 continue;
             }
             let rank_value = self.rank_value(ctx, day, &candidate, &stock)?;
+            if !rank_value.is_finite() {
+                if diagnostics.len() < 12 {
+                    diagnostics.push(format!(
+                        "{} rejected by missing rank field",
+                        candidate.symbol
+                    ));
+                }
+                continue;
+            }
             candidates.push((candidate, stock, rank_value));
         }
         candidates.sort_by(|lhs, rhs| {
@@ -5070,6 +5107,120 @@ mod tests {
         }
     }
 
+    #[test]
+    fn platform_strategy_treats_missing_stock_rolling_window_as_filter_reject() {
+        let date = d(2025, 2, 3);
+        let data = DataSet::from_components(
+            vec![Instrument {
+                symbol: "000001.SZ".to_string(),
+                name: "Short History Stock".to_string(),
+                board: "SZ".to_string(),
+                round_lot: 100,
+                listed_at: Some(d(2025, 1, 20)),
+                delisted_at: None,
+                status: "active".to_string(),
+            }],
+            vec![DailyMarketSnapshot {
+                date,
+                symbol: "000001.SZ".to_string(),
+                timestamp: Some("2025-02-03 10:18:00".to_string()),
+                day_open: 10.0,
+                open: 10.0,
+                high: 10.3,
+                low: 9.9,
+                close: 10.1,
+                last_price: 10.05,
+                bid1: 10.04,
+                ask1: 10.05,
+                prev_close: 9.95,
+                volume: 1_000_000,
+                tick_volume: 10_000,
+                bid1_volume: 2_000,
+                ask1_volume: 2_000,
+                trading_phase: Some("continuous".to_string()),
+                paused: false,
+                upper_limit: 10.95,
+                lower_limit: 8.95,
+                price_tick: 0.01,
+            }],
+            vec![DailyFactorSnapshot {
+                date,
+                symbol: "000001.SZ".to_string(),
+                market_cap_bn: 12.0,
+                free_float_cap_bn: 10.0,
+                pe_ttm: 8.0,
+                turnover_ratio: Some(22.0),
+                effective_turnover_ratio: Some(18.0),
+                extra_factors: BTreeMap::new(),
+            }],
+            vec![CandidateEligibility {
+                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,
+            }],
+            vec![BenchmarkSnapshot {
+                date,
+                benchmark: "000852.SH".to_string(),
+                open: 1000.0,
+                close: 1002.0,
+                prev_close: 998.0,
+                volume: 1_000_000,
+            }],
+        )
+        .expect("dataset");
+        let portfolio = PortfolioState::new(1_000_000.0);
+        let subscriptions = BTreeSet::new();
+        let ctx = StrategyContext {
+            execution_date: date,
+            decision_date: date,
+            decision_index: 0,
+            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.refresh_rate = 1;
+        cfg.max_positions = 1;
+        cfg.benchmark_short_ma_days = 1;
+        cfg.benchmark_long_ma_days = 1;
+        cfg.market_cap_lower_expr = "0".to_string();
+        cfg.market_cap_upper_expr = "100".to_string();
+        cfg.selection_limit_expr = "1".to_string();
+        cfg.stock_filter_expr = "rolling_mean(\"volume\", 60) > 0".to_string();
+        let mut strategy = PlatformExprStrategy::new(cfg);
+
+        let decision = strategy.on_day(&ctx).expect("platform decision");
+
+        assert!(decision.order_intents.is_empty());
+        assert!(
+            decision
+                .diagnostics
+                .iter()
+                .any(|item| item.contains("selected=0"))
+        );
+        assert!(
+            decision
+                .diagnostics
+                .iter()
+                .any(|item| item.contains("000001.SZ rejected by stock_expr"))
+        );
+    }
+
     #[test]
     fn platform_strategy_emits_target_shares_explicit_action() {
         let date = d(2025, 2, 3);
diff --git a/crates/fidc-core/src/strategy_ai.rs b/crates/fidc-core/src/strategy_ai.rs
index 2ff8c51..479e128 100644
--- a/crates/fidc-core/src/strategy_ai.rs
+++ b/crates/fidc-core/src/strategy_ai.rs
@@ -177,8 +177,8 @@ pub fn built_in_strategy_manual() -> StrategyAiManual {
                     ManualField { name: "symbol_open_order_count/symbol_open_buy_qty/symbol_open_sell_qty/latest_symbol_open_order_id".to_string(), field_type: "int".to_string(), detail: "当前证券在挂单簿中的未成交挂单摘要和最近挂单 id。".to_string() },
                     ManualField { name: "latest_symbol_open_order_status/latest_symbol_open_order_unfilled_qty".to_string(), field_type: "string/int".to_string(), detail: "当前证券最近一笔挂单的状态和未成交数量。".to_string() },
                     ManualField { name: "in_dynamic_universe/is_subscribed".to_string(), field_type: "bool".to_string(), detail: "当前证券是否在动态 universe 内，以及是否仍在订阅集合中。".to_string() },
-                    ManualField { name: "stock_ma5/stock_ma10/stock_ma20/stock_ma30".to_string(), field_type: "float".to_string(), detail: "个股价格均线内建别名。只内建这几个窗口；15 日、45 日等任意窗口请改用 sma(\"close\", n)。".to_string() },
-                    ManualField { name: "stock_volume_ma5/stock_volume_ma10/stock_volume_ma20/stock_volume_ma60".to_string(), field_type: "float".to_string(), detail: "个股成交量均线内建别名。只内建这几个窗口；任意窗口请改用 rolling_mean(\"volume\", n)。".to_string() },
+                    ManualField { name: "stock_ma5/stock_ma10/stock_ma20/stock_ma30".to_string(), field_type: "float".to_string(), detail: "个股价格均线内建别名，按当前交易日前 N 个已完成交易日的收盘价计算；历史窗口不足时为 NaN，比较条件会自然不通过；15 日、45 日等任意窗口请改用 sma(\"close\", n)。".to_string() },
+                    ManualField { name: "stock_volume_ma5/stock_volume_ma10/stock_volume_ma20/stock_volume_ma60".to_string(), field_type: "float".to_string(), detail: "个股成交量均线内建别名，按当前交易日前 N 个已完成交易日的成交量计算，不包含回测当天未来成交量；历史窗口不足时为 NaN，比较条件会自然不通过；任意窗口请改用 rolling_mean(\"volume\", n)。".to_string() },
                     ManualField { name: "factors[\"field\"] / factor(\"field\")".to_string(), field_type: "float/string".to_string(), detail: "当前证券当日数据库因子。数值字段返回数字，字符串字段返回字符串；字符串字段名如果是合法标识符，也可直接写字段名，例如 concept == \"ai_chip\"。".to_string() },
                     ManualField { name: "listed_days".to_string(), field_type: "int".to_string(), detail: "上市天数。".to_string() },
                 ],
@@ -229,7 +229,7 @@ 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。任意成交量窗口推荐用它，比如 rolling_mean(\"volume\", 15)。".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: "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: "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() },