//! Completed-session OHLCV rules shared by research and strategy execution.
use crate::DataSet;
use chrono::{FixedOffset, NaiveDate, TimeZone};
use serde::{Deserialize, Serialize};
use serde_json::{Value, json};
use std::collections::{BTreeMap, BTreeSet};

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。"},
        "session_event":{"label":"已完成分钟事件","parameters":{"opening_minutes":[30,1,120],"volume_window":[5,2,120],"volume_multiple":[3.0,1,20]},"stages":["selection","buy","sell"],"method":"仅本交易日完整分钟OHLCVA，信号K线必须早于执行时点；不使用盘口快照伪造K线。"},
        "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。"},
        "mean_volume_spike":{"label":"均量倍增","parameters":{"volume_window":[5,2,60],"volume_multiple":[3.0,1,10]},"stages":["selection","buy"],"method":"量达到此前N个交易日均量的M倍且当日上涨。分母不含当日；保留与最大量规则的区别。"},
        "mean_shrink_breakout":{"label":"倍量后缩量阳线突破","parameters":{"spike_lookback":[5,2,30],"volume_window":[5,2,60],"volume_multiple":[3.0,1,10],"shrink_ratio":[0.5,0.01,1]},"stages":["selection","buy"],"method":"此前出现N日均量M倍放量，当前缩量阳线收盘突破该放量日最高价。"},
        "breakout_retest":{"label":"突破回踩站回","parameters":{"high_window":[60,5,252],"retest_lookback":[10,2,30],"price_tolerance":[0.02,0,0.2],"shrink_ratio":[0.8,0.01,1]},"stages":["selection","buy"],"method":"观察窗先收盘突破此前N日最高价，随后低点回踩突破位容差区，今日收盘站回该位且不低于昨日、成交量收缩。突破与回踩不得同日。"},
        "limit_consolidation":{"label":"涨停后整理（日线）","parameters":{"anchor_lag":[4,2,30],"price_band":[0.05,0,0.3],"volume_band":[0.15,0,2],"ma_window":[5,2,60]},"stages":["selection","buy"],"method":"明确T-i日按真实涨停价收盘，后续收盘和量相对锚日偏离受限，今日收盘低于完整日线均线；不是盘中动态MA条件。"},
        "shrink_breakout":{"label":"缩量突破","parameters":{"spike_lookback":[5,2,30],"volume_window":[5,2,60],"volume_multiple":[3.0,1,10],"shrink_ratio":[0.5,0.01,1]},"stages":["selection","buy"],"method":"此前观察窗有放量日，今日收盘超过该日最高价，成交量不超过其指定比例。"},
        "ma_below":{"label":"均线下方","parameters":{"ma_window":[20,2,252]},"stages":["sell"],"method":"完整收盘价低于含当日的N日均线；独立卖出条件。"},
        "volume_down":{"label":"放量下跌","parameters":{"volume_window":[5,2,60],"volume_multiple":[3.0,1,10]},"stages":["sell"],"method":"当日下跌且量达到此前N日最大量的指定倍数。"}
    },"data_frequency":"1d","execution_policies":["next_session_open"]})
}

#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(deny_unknown_fields)]
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>,
    #[serde(default, skip_serializing_if = "Option::is_none")]
    pub execution_context: Option<crate::pattern_context::ExecutionContext>,
    #[serde(default,skip_serializing_if="Option::is_none")]
    pub session_event:Option<String>,
}
impl PatternSpec {
    pub fn validate(self) -> Result<Self, String> {
        if self.template=="session_event" {
            if !self.session_event.as_deref().is_some_and(|id|crate::session_events::EVENTS.contains(&id)) || self.execution_context.is_some() {return Err("session_event_contract_invalid".into());}
        } else if self.session_event.is_some() {return Err("unexpected_session_event_id".into());}
        let allowed = if let Some(context) = &self.execution_context {
            context.validate(self.expression.as_ref().ok_or("pattern_context_requires_expression")?)?;
            crate::pattern_context::CONTEXT_FIELDS
        } else { &[] };
        let spec = self.validate_with_context(allowed)?;
        if let Some(context) = &spec.execution_context {
            if context.rank_universe.len().saturating_mul(spec.history_len()) > 2_000_000 {
                return Err("pattern_rank_window_budget_exceeded: 完整截面不得截断".into());
            }
        }
        Ok(spec)
    }
    fn validate_with_context(mut self, context_fields: &[&str]) -> 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()) && !context_fields.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)
            .ok_or("未登记的量价模板")?;
        let parameters = definition["parameters"].as_object().unwrap();
        if self.parameters.keys().any(|k| !parameters.contains_key(k)) {
            return Err("模板包含未知参数".into());
        }
        for (key, bounds) in parameters {
            let value = self.parameters.get(key).unwrap_or(&bounds[0]);
            let number = value
                .as_f64()
                .filter(|v| v.is_finite())
                .ok_or_else(|| format!("{key}必须为有限数值"))?;
            if number < bounds[1].as_f64().unwrap() || number > bounds[2].as_f64().unwrap() {
                return Err(format!("{key}超出允许范围"));
            }
            if key.ends_with("window") || key.ends_with("lookback") || key == "anchor_lag" || key=="opening_minutes" {
                if number.fract() != 0.0 {
                    return Err(format!("{key}必须是整数"));
                }
                self.parameters.insert(key.clone(), json!(number as usize));
            } else {
                self.parameters.insert(key.clone(), json!(number));
            }
        }
        if self.template == "strength" && self.n("fast_window") >= self.n("slow_window") {
            return Err("短均线必须小于长均线".into());
        }
        Ok(self)
    }
    pub fn n(&self, key: &str) -> usize {
        self.parameters[key].as_u64().unwrap() as usize
    }
    pub fn v(&self, key: &str) -> f64 {
        self.parameters[key].as_f64().unwrap()
    }
    pub fn history_len(&self) -> usize {
        match self.template.as_str() {
            "session_event"=>1,
            "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" | "mean_volume_spike" => self.n("volume_window") + 1,
            "breakout_retest" => self.n("high_window") + self.n("retest_lookback") + 1,
            "limit_consolidation" => (self.n("anchor_lag")+1).max(self.n("ma_window")),
            "ma_below" => self.n("ma_window").max(2),
            "shrink_breakout" | "mean_shrink_breakout" => self.n("spike_lookback") + self.n("volume_window") + 1,
            _ => unreachable!(),
        }
    }
}

#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(deny_unknown_fields)]
pub struct PatternBar {
    pub date: NaiveDate,
    pub open: Option<f64>,
    pub high: Option<f64>,
    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>,
    #[serde(default)]
    pub upper_limit: Option<f64>,
    #[serde(default)]
    pub no_limit: Option<bool>,
    pub adjustment_factor_backward1: Option<f64>,
    pub paused: Option<bool>,
    #[serde(default)]
    pub source_path: Option<String>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(deny_unknown_fields)]
pub struct PatternSeries {
    pub symbol: String,
    #[serde(default)]
    pub name: Option<String>,
    #[serde(default)]
    pub listed_at: Option<NaiveDate>,
    pub bars: Vec<PatternBar>,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternResult {
    pub symbol: String,
    pub name: Option<String>,
    pub matched: bool,
    pub score: Option<f64>,
    pub checks: Vec<Value>,
    pub values: Value,
    pub anchor: Value,
    pub exclusion: Option<Value>,
}

fn number(v: Option<f64>, symbol: &str, day: NaiveDate, field: &str) -> Result<f64, String> {
    v.filter(|v|v.is_finite()).ok_or_else(||format!("pattern_input_invalid: symbol={symbol}, date={day}, field={field}, reason=missing_or_nonfinite"))
}
fn check(checks: &mut Vec<Value>, label: &str, actual: f64, operator: &str, threshold: f64) {
    let passed = match operator {
        ">" => actual > threshold,
        "<" => actual < threshold,
        ">=" => actual >= threshold,
        "<=" => actual <= threshold,
        _ => false,
    };
    checks.push(json!({"label":label,"actual":actual,"operator":operator,"threshold":threshold,"passed":passed}));
}

fn mean(mut values: impl ExactSizeIterator<Item = f64>) -> Result<f64, String> {
    let count = values.len();
    let first = values.next().ok_or("pattern_mean_empty")?;
    // Center before summation so an unchanged decimal price stays exactly unchanged.
    let result = first
        + values
            .map(|value| (value - first) / count as f64)
            .sum::<f64>();
    if !result.is_finite() {
        return Err("pattern_mean_nonfinite".into());
    }
    Ok(result)
}

/// No calendar compression, fill-forward prices or numerical substitutes.
pub fn evaluate(
    spec: &PatternSpec,
    days: &[NaiveDate],
    series: &PatternSeries,
) -> Result<PatternResult, String> {
    evaluate_with_context(spec, days, series, &BTreeMap::new(), false)
}

pub(crate) fn evaluate_with_context(
    spec: &PatternSpec,
    days: &[NaiveDate],
    series: &PatternSeries,
    context: &BTreeMap<String, Vec<Option<f64>>>,
    numeric_output: bool,
) -> Result<PatternResult, String> {
    if spec.template=="session_event" {return Err("session_event_requires_completed_minute_endpoint".into());}
    if days.len() != spec.history_len() || days.windows(2).any(|w| w[0] >= w[1]) {
        return Err("pattern_calendar_incomplete: 需要完整、唯一且递增的真实交易日窗口".into());
    }
    let by_day = series
        .bars
        .iter()
        .map(|b| (b.date, b))
        .collect::<BTreeMap<_, _>>();
    if by_day.len() != series.bars.len() || series.bars.iter().any(|b| days.binary_search(&b.date).is_err()) {
        return Err(format!(
            "pattern_input_invalid: symbol={}, reason=duplicate_or_out_of_scope",
            series.symbol
        ));
    }
    let mut unavailable = Vec::new();
    let mut prices = Vec::new();
    for &day in days {
        let Some(b) = by_day.get(&day) else {
            if series.listed_at.is_some_and(|listed| day < listed) {
                unavailable.push(
                    json!({"date":day,"reason":"before_listing","listed_at":series.listed_at}),
                );
                continue;
            }
            return Err(format!(
                "pattern_input_invalid: symbol={}, date={day}, reason=missing_market_row",
                series.symbol
            ));
        };
        let factor = if b.close.is_some_and(|c| c.is_finite() && c > 0.0) {
            let factor = number(
                b.adjustment_factor_backward1,
                &series.symbol,
                day,
                "adjustment_factor_backward1",
            )?;
            if factor <= 0.0 {
                return Err(format!(
                    "pattern_input_invalid: symbol={}, date={day}, field=adjustment_factor_backward1, reason=nonpositive",
                    series.symbol
                ));
            }
            factor
        } else {
            1.0
        };
        let paused = b.paused.ok_or_else(|| {
            format!(
                "pattern_input_invalid: symbol={}, date={day}, field=paused",
                series.symbol
            )
        })?;
        if paused {
            unavailable.push(
                json!({"date":day,"reason":"confirmed_suspension","source_path":b.source_path}),
            );
            continue;
        }
        if series.listed_at.is_some_and(|listed| day < listed) {
            return Err(format!(
                "pattern_input_invalid: symbol={}, date={day}, reason=price_before_listing",
                series.symbol
            ));
        }
        let o = number(b.open, &series.symbol, day, "open")?;
        let h = number(b.high, &series.symbol, day, "high")?;
        let l = number(b.low, &series.symbol, day, "low")?;
        let c = number(b.close, &series.symbol, day, "close")?;
        let v = number(b.volume, &series.symbol, day, "volume")?;
        if o <= 0.0 || l <= 0.0 || c <= 0.0 || h < o.max(c) || l > o.min(c) || v < 0.0 {
            return Err(format!(
                "pattern_input_invalid: symbol={}, date={day}, reason=invalid_ohlcv",
                series.symbol
            ));
        }
        prices.push((o * factor, h * factor, l * factor, c * factor, v));
    }
    let mut result = PatternResult {
        symbol: series.symbol.clone(),
        name: series.name.clone(),
        matched: false,
        score: None,
        checks: vec![],
        values: json!({}),
        anchor: Value::Null,
        exclusion: None,
    };
    if !unavailable.is_empty() {
        result.exclusion = Some(
            json!({"reason":"proven_incomplete_window","signal_date":days.last(),"evidence":unavailable}),
        );
        return Ok(result);
    }
    let len = prices.len();
    let (o, h, l, c, v) = prices[len - 1];
    let change = c / prices[len - 2].3 - 1.0;
    result.values = json!({"close":by_day[&days[len-1]].close,"daily_return":change});
    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: symbol={}, date={d}, field={name}, reason=invalid_value",
                                series.symbol
                            ));
                        }
                        Ok(Some(value))
                    })
                    .collect::<Result<Vec<_>, String>>()?;
                fields.insert(name.into(), values);
            }
            for (name, values) in context {
                if fields.contains_key(name) || values.len() != days.len()
                    || values.iter().flatten().any(|v| !v.is_finite()) {
                    return Err(format!("research_context_invalid: {} {name}", series.symbol));
                }
                fields.insert(name.clone(), values.clone());
            }
            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 numeric_output {
                if values.value_type != crate::factor_events::ValueType::Number {
                    return Err("research_rank_input_requires_numeric_expression".into());
                }
                return Ok(result);
            }
            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))?;
            let momentum = c / prices[len - 1 - spec.n("momentum_window")].3 - 1.0;
            score = Some(momentum);
            result.values["momentum"] = json!(momentum);
            result.values["fast_ma"] = json!(fast);
            result.values["slow_ma"] = json!(slow);
            check(&mut result.checks, "收盘高于短均线", c, ">", fast);
            check(&mut result.checks, "短均线高于长均线", fast, ">", slow);
        }
        "breakout" => {
            let prior_high = prices[len - 1 - spec.n("high_window")..len - 1]
                .iter()
                .map(|b| b.1)
                .fold(f64::NEG_INFINITY, f64::max);
            let avg = mean(
                prices[len - 1 - spec.n("volume_window")..len - 1]
                    .iter()
                    .map(|b| b.4),
            )?;
            if avg <= 0.0 {
                return Err(format!(
                    "pattern_input_invalid: symbol={}, reason=zero_reference_volume",
                    series.symbol
                ));
            }
            let shadow = if h > l { (h - o.max(c)) / (h - l) } else { 0.0 };
            score = Some(c / prior_high - 1.0);
            result.values["volume_ratio"] = json!(v / avg);
            result.values["upper_shadow"] = json!(shadow);
            check(&mut result.checks, "收盘突破前高", c, ">", prior_high);
            check(
                &mut result.checks,
                "均量倍数",
                v / avg,
                ">=",
                spec.v("volume_multiple"),
            );
            check(
                &mut result.checks,
                "上影比例",
                shadow,
                "<=",
                spec.v("max_upper_shadow"),
            );
        }
        "volume_spike" | "volume_down" | "mean_volume_spike" => {
            let reference = &prices[len - 1 - spec.n("volume_window")..len - 1];
            let high = if spec.template=="mean_volume_spike" {mean(reference.iter().map(|b|b.4))?} else {reference
                .iter()
                .map(|b| b.4)
                .fold(0.0, f64::max)};
            if high <= 0.0 {
                return Err(format!(
                    "pattern_input_invalid: symbol={}, reason=zero_reference_volume",
                    series.symbol
                ));
            }
            score = Some(v / high);
            result.values["volume_ratio"] = json!(v / high);
            check(
                &mut result.checks,
                if spec.template=="mean_volume_spike" {"均量倍数"} else {"最大量倍数"},
                v / high,
                ">=",
                spec.v("volume_multiple"),
            );
            check(
                &mut result.checks,
                if spec.template != "volume_down" {
                    "当日上涨"
                } else {
                    "当日下跌"
                },
                change,
                if spec.template != "volume_down" {
                    ">"
                } else {
                    "<"
                },
                0.0,
            );
        }
        "ma_below" => {
            let avg = mean(prices[len - spec.n("ma_window")..].iter().map(|b| b.3))?;
            score = Some(avg / c - 1.0);
            result.values["ma"] = json!(avg);
            check(&mut result.checks, "收盘低于均线", c, "<", avg);
        }
        "shrink_breakout" | "mean_shrink_breakout" => {
            let mut spikes = Vec::new();
            let mut eligible = Vec::new();
            for i in len - 1 - spec.n("spike_lookback")..len - 1 {
                let reference=&prices[i - spec.n("volume_window")..i];
                let prior = if spec.template=="mean_shrink_breakout"{mean(reference.iter().map(|b|b.4))?}else{reference
                    .iter()
                    .map(|b| b.4)
                    .fold(0.0, f64::max)};
                if prior <= 0.0 {
                    return Err(format!(
                        "pattern_input_invalid: symbol={}, date={}, reason=zero_reference_volume",
                        series.symbol, days[i]
                    ));
                }
                if prices[i].4 >= prior * spec.v("volume_multiple") {
                    spikes.push(i);
                    if c > prices[i].1 && v <= prices[i].4 * spec.v("shrink_ratio") {
                        eligible.push(i);
                    }
                }
            }
            check(
                &mut result.checks,
                "观察窗存在放量日",
                spikes.len() as f64,
                ">",
                0.0,
            );
            if let Some(&i) = eligible.last().or_else(|| spikes.last()) {
                score = Some(c / prices[i].1 - 1.0);
                result.values["spike_date"] = json!(days[i]);
                result.values["volume_ratio"] = json!(v / prices[i].4);
                check(
                    &mut result.checks,
                    "收盘突破放量日高点",
                    c,
                    ">",
                    prices[i].1,
                );
                check(
                    &mut result.checks,
                    "缩量比例",
                    v / prices[i].4,
                    "<=",
                    spec.v("shrink_ratio"),
                );
            }
            if spec.template=="mean_shrink_breakout" {check(&mut result.checks,"当前为阳线",c,">",o);}
        }
        "breakout_retest" => {
            let mut anchors=Vec::new();let mut eligible=Vec::new();
            for i in len-1-spec.n("retest_lookback")..len-1 {
                let level=prices[i-spec.n("high_window")..i].iter().map(|b|b.1).fold(f64::NEG_INFINITY,f64::max);
                if prices[i].3<=level {continue;}
                let retraced=prices[i+1..].iter().any(|b| b.2 <= level*(1.0+spec.v("price_tolerance")));
                anchors.push((i,level,retraced));
                if retraced&&c>=level&&c>=prices[len-2].3&&prices[i].4>0.0&&v<=prices[i].4*spec.v("shrink_ratio") {eligible.push((i,level,retraced));}
            }
            check(&mut result.checks,"观察窗存在先前突破",anchors.len() as f64,">",0.0);
            if let Some(&(i,level,retraced))=eligible.last().or_else(||anchors.last()) {
                result.values["breakout_date"]=json!(days[i]);result.values["breakout_level"]=json!(level);result.values["days_since_breakout"]=json!(len-1-i);
                check(&mut result.checks,"突破后曾回踩",if retraced{1.0}else{0.0},">",0.0);
                check(&mut result.checks,"收盘重新站回突破位",c,">=",level);
                check(&mut result.checks,"收盘不低于昨日",c,">=",prices[len-2].3);
                if prices[i].4<=0.0{return Err("突破锚日成交量为零，不能计算缩量比例".into());}
                check(&mut result.checks,"相对突破日缩量",v/prices[i].4,"<=",spec.v("shrink_ratio"));
                score=Some(c/level-1.0);
            }
        }
        "limit_consolidation" => {
            let i=len-1-spec.n("anchor_lag");let anchor=by_day[&days[i]];
            let is_limit=if anchor.no_limit==Some(true){false}else{
                let upper=number(anchor.upper_limit,&series.symbol,days[i],"upper_limit")?;
                if upper<=0.0||upper>=99999.0{return Err("涨停事件缺少有效源涨停价或无涨跌幅限制证据，禁止按比例推算".into());}
                (anchor.close.unwrap()/upper-1.0).abs()<=1e-8
            };
            if prices[i].4<=0.0{return Err("涨停锚日成交量为零".into());}
            let price_gap=prices[i+1..].iter().map(|b|(b.3/prices[i].3-1.0).abs()).fold(0.0,f64::max);
            let volume_gap=prices[i+1..].iter().map(|b|(b.4/prices[i].4-1.0).abs()).fold(0.0,f64::max);
            let avg=mean(prices[len-spec.n("ma_window")..].iter().map(|b|b.3))?;
            result.values["limit_date"]=json!(days[i]);result.values["price_deviation"]=json!(price_gap);result.values["volume_deviation"]=json!(volume_gap);
            check(&mut result.checks,"锚日真实涨停收盘",if is_limit{1.0}else{0.0},">",0.0);
            check(&mut result.checks,"后续收盘最大偏离",price_gap,"<=",spec.v("price_band"));
            check(&mut result.checks,"后续成交量最大偏离",volume_gap,"<=",spec.v("volume_band"));
            check(&mut result.checks,"收盘低于日线均线",c,"<",avg);score=Some(avg/c-1.0);
        }
        _ => unreachable!(),
    }
    if score.is_some_and(|v| !v.is_finite()) {
        return Err("pattern_result_nonfinite".into());
    }
    result.score = score;
    result.matched = result.checks.iter().all(|c| c["passed"] == true);
    Ok(result)
}

pub fn evaluate_dataset(
    spec: &PatternSpec,
    data: &DataSet,
    date: NaiveDate,
    symbol: &str,
) -> Result<PatternResult, String> {
    let context = crate::pattern_context::build_dataset_context(spec, data, date)?;
    evaluate_dataset_context(spec, data, date, symbol, &context)
}

pub fn dataset_series(data: &DataSet, days: &[NaiveDate], symbol: &str) -> PatternSeries {
    let bars = days.iter().filter_map(|&d| data.market(d, symbol).map(|b| PatternBar {
        date:d, open:Some(b.open), high:Some(b.high), low:Some(b.low), close:Some(b.close),
        volume:Some(b.volume as f64), prev_close:Some(b.prev_close),
        amount:data.factor_numeric_value(d,symbol,"amount"),upper_limit:Some(b.upper_limit),
        no_limit:data.factor_numeric_value(d,symbol,"no_limit").map(|v|v==1.0),
        adjustment_factor_backward1:data.factor(d,symbol).and_then(|f|f.adjustment_factor_backward1),
        paused:Some(b.paused), source_path:None,
    })).collect();
    PatternSeries{symbol:symbol.into(),name:data.instrument(symbol).map(|i|i.name.clone()),
        listed_at:data.instrument(symbol).and_then(|i|i.listed_at),bars}
}

pub fn evaluate_dataset_context(
    spec: &PatternSpec, data: &DataSet, date: NaiveDate, symbol: &str, context: &ResearchContext,
) -> Result<PatternResult,String> {
    let days = data.calendar().trailing_days(date, spec.history_len());
    let mut fields = context.common.clone();
    fields.extend(context.by_symbol.get(symbol).cloned().unwrap_or_default());
    let outside = spec.execution_context.as_ref().is_some_and(|c| c.rank_expression.is_some() && !c.rank_universe.iter().any(|s|s==symbol));
    if outside {
        for name in ["scope_rank","scope_percentile"] {fields.insert(name.into(),vec![None;days.len()]);}
        fields.insert("scope_size".into(),vec![Some(spec.execution_context.as_ref().unwrap().rank_universe.len() as f64);days.len()]);
    }
    let mut result = evaluate_with_context(spec,&days,&dataset_series(data,&days,symbol),&fields,false)?;
    if outside && result.score.is_none() { result.exclusion=Some(json!({"reason":"outside_frozen_rank_universe","symbol":symbol,"signal_date":date})); }
    result.values["execution_context_latest"]=json!(fields.iter().map(|(k,v)|(k,v.last().copied().flatten())).collect::<BTreeMap<_,_>>());
    Ok(result)
}

pub fn evaluate_batch(
    spec: PatternSpec,
    days: &[NaiveDate],
    series: &[PatternSeries],
) -> Result<Value, String> {
    evaluate_batch_with_policy(spec, days, series, false)
}

/// Partial results are research diagnostics, never strategy execution inputs.
pub fn evaluate_batch_with_policy(
    spec: PatternSpec, days: &[NaiveDate], series: &[PatternSeries], isolate_data_errors: bool,
) -> Result<Value, String> {
    let spec = spec.validate()?;
    if series.is_empty()
        || series.len() > 200
        || series
            .iter()
            .map(|s| &s.symbol)
            .collect::<BTreeSet<_>>()
            .len()
            != series.len()
    {
        return Err("pattern_batch_invalid: 需要1至200只唯一证券".into());
    }
    let rows = series
        .iter()
        .map(|s| research_row(evaluate(&spec, days, s), s, isolate_data_errors))
        .collect::<Result<Vec<_>, _>>()?;
    Ok(
        json!({"contract":CONTRACT,"spec":spec,"required_history":spec.history_len(),"rows":rows,"read_only":true}),
    )
}

fn research_row(result: Result<PatternResult, String>, series: &PatternSeries, isolate: bool) -> Result<Value, String> {
    match result {
        Ok(row) => Ok(json!(row)),
        Err(detail) if isolate && detail.starts_with(&format!("pattern_input_invalid: symbol={},", series.symbol)) => {
            let fields = detail.split(", ").filter_map(|p| p.split_once('=')).collect::<BTreeMap<_,_>>();
            Ok(json!({"symbol":series.symbol,"name":series.name,"matched":null,"score":null,
                "checks":[],"values":{},"anchor":null,"exclusion":null,
                "data_issue":{"reason":fields.get("reason"),"date":fields.get("date"),"field":fields.get("field"),"detail":detail}}))
        },
        Err(error) => Err(error),
    }
}

/// Values are supplied only by the verified research transport or dataset context builder.
#[derive(Debug, Clone, Default, Deserialize)]
#[serde(deny_unknown_fields)]
pub struct ResearchContext {
    #[serde(default)]
    pub common: BTreeMap<String, Vec<Option<f64>>>,
    #[serde(default)]
    pub by_symbol: BTreeMap<String, BTreeMap<String, Vec<Option<f64>>>>,
}

pub fn evaluate_research_batch(
    spec: PatternSpec, days: &[NaiveDate], series: &[PatternSeries],
    context: &ResearchContext, numeric_output: bool,
) -> Result<Value, String> {
    evaluate_research_batch_with_policy(spec, days, series, context, numeric_output, false)
}

pub fn evaluate_research_batch_with_policy(
    spec: PatternSpec, days: &[NaiveDate], series: &[PatternSeries],
    context: &ResearchContext, numeric_output: bool, isolate_data_errors: bool,
) -> Result<Value, String> {
    let common_fields = ["index_open", "index_high", "index_low", "index_close"].into_iter()
        .chain(crate::market_event_context::COMMON_FIELDS.iter().copied()).collect::<Vec<_>>();
    let symbol_fields = ["scope_rank", "scope_percentile", "scope_size"].into_iter()
        .chain(crate::market_event_context::INDUSTRY_FIELDS.iter().copied()).collect::<Vec<_>>();
    if spec.template != "expression" || series.is_empty() || series.len() > 200
        || series.iter().map(|s| &s.symbol).collect::<BTreeSet<_>>().len() != series.len()
        || context.common.keys().any(|k| !common_fields.contains(&k.as_str()))
        || context.by_symbol.iter().any(|(s, fields)| !series.iter().any(|row| &row.symbol == s)
            || fields.keys().any(|k| !symbol_fields.contains(&k.as_str()))) {
        return Err("research_context_scope_or_fields_invalid".into());
    }
    for (name, values) in &context.common {
        if values.len() != days.len() || values.iter().any(|v| if name.starts_with("index_") {
            !v.is_some_and(|x| x.is_finite() && x > 0.0)
        } else { v.is_some_and(|x| !x.is_finite()) }) {
            return Err(format!("research_index_window_incomplete: {name}"));
        }
    }
    let allowed = common_fields.into_iter().chain(symbol_fields).collect::<Vec<_>>();
    let spec = spec.validate_with_context(&allowed)?;
    let dependencies = crate::factor_events::field_dependencies(spec.expression.as_ref().unwrap());
    let mut rows = Vec::with_capacity(series.len());
    for item in series {
        let mut fields = context.common.clone();
        fields.extend(context.by_symbol.get(&item.symbol).cloned().unwrap_or_default());
        if allowed.iter().any(|f| dependencies.contains(*f) && !fields.contains_key(*f)) {
            return Err(format!("research_context_missing: {}", item.symbol));
        }
        for (name, values) in &fields {
            if values.len() != days.len() || values.iter().flatten().any(|v| !v.is_finite()
                || (name == "scope_percentile" && !(0.0..=1.0).contains(v))
                || (matches!(name.as_str(), "scope_rank" | "scope_size") && *v < 1.0)) {
                return Err(format!("research_context_invalid: {} {name}", item.symbol));
            }
        }
        let mut result = research_row(evaluate_with_context(&spec, days, item, &fields, numeric_output), item, isolate_data_errors)?;
        result["values"]["research_context_latest"] = json!(fields.iter().map(|(k,v)|(k,v.last().copied().flatten())).collect::<BTreeMap<_,_>>());
        rows.push(result);
    }
    Ok(json!({"contract":CONTRACT,"context_contract":"fidc_research_event_context_v1","spec":spec,
        "required_history":spec.history_len(),"rows":rows,"read_only":true,
        "source_evidence_verified":false,"live_routing":false,"rule_backtest_supported":false}))
}

pub fn expression_specs(expression: &str) -> Result<Vec<PatternSpec>, String> {
    let mut specs = Vec::new();
    for helper in ["pattern_signal", "pattern_score"] {
        for (index, _) in expression.match_indices(helper) {
            if index > 0
                && expression[..index]
                    .chars()
                    .next_back()
                    .is_some_and(|c| c.is_alphanumeric() || c == '_')
            {
                continue;
            }
            let rest = expression[index + helper.len()..].trim_start();
            let Some(rest) = rest.strip_prefix('(') else {
                continue;
            };
            let rest = rest.trim_start();
            let mut stream = serde_json::Deserializer::from_str(rest).into_iter::<String>();
            let text = stream
                .next()
                .ok_or("missing pattern JSON")?
                .map_err(|e| e.to_string())?;
            if !rest[stream.byte_offset()..].trim_start().starts_with(')') {
                return Err("pattern helper takes one JSON string".into());
            }
            let spec: PatternSpec = serde_json::from_str(&text).map_err(|e| e.to_string())?;
            specs.push(spec.validate()?);
        }
    }
    Ok(specs)
}

#[cfg(test)]
mod tests {
    use super::*;
    #[test]
    fn research_isolates_missing_listing_day_without_weakening_execution() {
        let days = ["2026-06-11", "2026-06-12"].map(|d|d.parse::<NaiveDate>().unwrap());
        let spec: PatternSpec = serde_json::from_value(json!({"template":"ma_below","parameters":{"ma_window":2}})).unwrap();
        let make = |symbol: &str| -> PatternSeries { serde_json::from_value(json!({"symbol":symbol,"listed_at":"2026-06-11","bars":days.map(|d|json!({"date":d,"open":10.,"high":11.,"low":9.,"close":10.,"volume":100.,"adjustment_factor_backward1":1.,"paused":false,"source_path":"/controlled/source.parquet"}))})).unwrap() };
        let complete=make("300395.SZ");let mut missing=make("920083.BJ");missing.bars.remove(0);
        let members=[complete.clone(),missing];
        assert!(evaluate_batch(spec.clone(),&days,&members).unwrap_err().contains("missing_market_row"));
        let partial=evaluate_batch_with_policy(spec.clone(),&days,&members,true).unwrap();
        assert_eq!(partial["rows"][0],json!(evaluate(&spec.validate().unwrap(),&days,&complete).unwrap()));
        assert!(partial["rows"][1]["matched"].is_null());
        assert_eq!(partial["rows"][1]["data_issue"]["date"],"2026-06-11");
        assert_eq!(partial["rows"][1]["data_issue"]["reason"],"missing_market_row");
        let invalid:PatternSpec=serde_json::from_value(json!({"template":"not-a-template"})).unwrap();
        assert!(evaluate_batch_with_policy(invalid,&days,&members,true).is_err());
    }
    #[test]
    fn research_index_and_ranking_context_never_unlock_strategy_mapping() {
        let days=["2026-09-04","2026-09-07","2026-09-08"].map(|s|s.parse::<NaiveDate>().unwrap());
        let spec:PatternSpec=serde_json::from_value(json!({"template":"expression","parameters":{"history_window":3},
            "expression":{"kind":"operator","name":"CROSS_ABOVE","args":[{"kind":"field","name":"close"},{"kind":"field","name":"index_close"}]}})).unwrap();
        assert!(spec.clone().validate().unwrap_err().contains("mapping_required"));
        let series:PatternSeries=serde_json::from_value(json!({"symbol":"TEST","bars":days.iter().zip([9.0,10.0,11.0]).map(|(d,c)|json!({"date":d,"open":c,"high":c,"low":c,"close":c,"volume":100.0,"adjustment_factor_backward1":1.0,"paused":false})).collect::<Vec<_>>()})).unwrap();
        let mut context=ResearchContext{common:BTreeMap::from([("index_close".into(),vec![Some(10.0);3])]),..Default::default()};
        let result=evaluate_research_batch(spec.clone(),&days,&[series.clone()],&context,false).unwrap();
        assert_eq!(result["rows"][0]["matched"],true);
        assert_eq!(result["source_evidence_verified"],false);
        assert_eq!(result["rule_backtest_supported"],false);
        context.common.get_mut("index_close").unwrap()[1]=None;
        assert!(evaluate_research_batch(spec.clone(),&days,&[series.clone()],&context,false).unwrap_err().contains("index_window_incomplete"));
        context.common=BTreeMap::from([("close".into(),vec![Some(10.0);3])]);
        assert!(evaluate_research_batch(spec,&days,&[series],&context,false).is_err());
    }

    #[test]
    fn research_numeric_output_keeps_warmup_unknown_without_a_false_signal() {
        let days=["2026-09-04","2026-09-07","2026-09-08"].map(|s|s.parse::<NaiveDate>().unwrap());
        let spec:PatternSpec=serde_json::from_value(json!({"template":"expression","parameters":{"history_window":3},
            "expression":{"kind":"operator","name":"PCT_CHANGE","window":2,"args":[{"kind":"field","name":"close"}]}})).unwrap();
        let series:PatternSeries=serde_json::from_value(json!({"symbol":"TEST","bars":days.iter().zip([10.0,10.5,11.0]).map(|(d,c)|json!({"date":d,"open":c,"high":c,"low":c,"close":c,"volume":100.0,"adjustment_factor_backward1":1.0,"paused":false})).collect::<Vec<_>>()})).unwrap();
        assert!(evaluate_batch(spec.clone(),&days,&[series.clone()]).is_err());
        let result=evaluate_research_batch(spec,&days,&[series],&ResearchContext::default(),true).unwrap();
        let values=&result["rows"][0]["values"]["expression"]["values"];
        assert!(values[0].is_null() && values[1].is_null());
        assert!((values[2].as_f64().unwrap()-0.1).abs()<1e-12);
        assert_eq!(result["rows"][0]["matched"],false);
    }
    #[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),
                        upper_limit: None,
                        no_limit: None,
                        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,
            execution_context: None,
            session_event: None,
        }
        .validate()
        .unwrap();
        let days = (0..spec.history_len())
            .map(|n| {
                NaiveDate::from_ymd_opt(2025, 1, 1).unwrap() + chrono::Duration::days(n as i64)
            })
            .collect::<Vec<_>>();
        let bars = days
            .iter()
            .enumerate()
            .map(|(n, &date)| {
                let c = 10.0 + n as f64;
                PatternBar {
                    date,
                    open: Some(c),
                    high: Some(c),
                    low: Some(c),
                    close: Some(c),
                    volume: Some(1000.0),
                    prev_close: Some(c - 1.0),
                    amount: Some(c * 1000.0),
                    upper_limit: None,
                    no_limit: None,
                    adjustment_factor_backward1: Some(1.0),
                    paused: Some(false),
                    source_path: Some("fixture.parquet".into()),
                }
            })
            .collect();
        (
            spec,
            days,
            PatternSeries {
                symbol: "000001.SZ".into(),
                name: None,
                listed_at: Some(NaiveDate::from_ymd_opt(1991, 4, 3).unwrap()),
                bars,
            },
        )
    }
    #[test]
    fn daily_patterns_all_templates_and_score_absence() {
        for template in [
            "strength",
            "breakout",
            "volume_spike",
            "shrink_breakout",
            "ma_below",
            "volume_down",
        ] {
            let (spec, days, series) = fixture(template);
            let result = evaluate(&spec, &days, &series).unwrap();
            assert_eq!(result.matched, template == "strength");
            assert_eq!(result.score.is_none(), template == "shrink_breakout");
        }
    }
    #[test]
    fn daily_patterns_adjusts_all_prices_not_volume() {
        let (spec, days, series) = fixture("strength");
        let a = evaluate(&spec, &days, &series).unwrap();
        let mut split = series.clone();
        for b in &mut split.bars {
            b.open = b.open.map(|p| p / 2.0);
            b.high = b.high.map(|p| p / 2.0);
            b.low = b.low.map(|p| p / 2.0);
            b.close = b.close.map(|p| p / 2.0);
            b.adjustment_factor_backward1 = Some(2.0);
        }
        let b = evaluate(&spec, &days, &split).unwrap();
        assert_eq!(a.score, b.score);
        assert_eq!(a.checks, b.checks);
    }

    #[test]
    fn mean_volume_is_not_prior_max_and_excludes_current_bar() {
        let (spec,days,mut series)=fixture("mean_volume_spike");
        for (bar,volume) in series.bars.iter_mut().zip([10.,10.,10.,10.,100.,100.]) {bar.volume=Some(volume);}
        assert!(evaluate(&spec,&days,&series).unwrap().matched);
        let mut old=spec.clone();old.template="volume_spike".into();
        assert!(!evaluate(&old,&days,&series).unwrap().matched);
        assert_eq!(evaluate(&spec,&days,&series).unwrap().values["volume_ratio"],json!(100./28.));
    }

    #[test]
    fn mean_volume_followup_requires_bullish_breakout_and_shrink() {
        let (spec,days,mut series)=fixture("mean_shrink_breakout");
        series.bars[6].volume=Some(4000.);
        let last=series.bars.last_mut().unwrap();last.open=Some(19.);last.low=Some(19.);
        assert!(evaluate(&spec,&days,&series).unwrap().matched);
        series.bars.last_mut().unwrap().volume=Some(3000.);
        assert!(!evaluate(&spec,&days,&series).unwrap().matched);
    }

    #[test]
    fn breakout_retest_needs_a_later_retest_not_the_breakout_candle_itself() {
        let (spec,days,mut series)=fixture("breakout_retest");
        for b in &mut series.bars {b.open=Some(10.);b.high=Some(10.);b.low=Some(10.);b.close=Some(10.);}
        let anchor=series.bars.len()-11;
        let b=&mut series.bars[anchor];b.open=Some(11.);b.high=Some(12.1);b.low=Some(9.9);b.close=Some(12.);b.volume=Some(2000.);
        for b in &mut series.bars[anchor+1..] {b.open=Some(10.3);b.high=Some(10.4);b.low=Some(10.3);b.close=Some(10.4);}
        assert!(!evaluate(&spec,&days,&series).unwrap().matched);
        series.bars[anchor+1].low=Some(9.95);
        assert!(evaluate(&spec,&days,&series).unwrap().matched);
    }

    #[test]
    fn limit_consolidation_requires_real_limit_and_never_infers_ten_percent() {
        let (spec,days,mut series)=fixture("limit_consolidation");
        for (b,c) in series.bars.iter_mut().zip([10.,10.1,10.2,10.1,9.9]) {b.open=Some(c);b.high=Some(c);b.low=Some(c);b.close=Some(c);}
        assert!(evaluate(&spec,&days,&series).unwrap_err().contains("upper_limit"));
        series.bars[0].upper_limit=Some(10.);
        assert!(evaluate(&spec,&days,&series).unwrap().matched);
        series.bars[0].no_limit=Some(true);
        assert!(!evaluate(&spec,&days,&series).unwrap().matched);
        series.bars[0].no_limit=Some(false);series.bars[0].upper_limit=Some(0.);
        assert!(evaluate(&spec,&days,&series).is_err());
    }

    #[test]
    fn daily_patterns_flat_decimal_prices_do_not_create_a_sell_signal() {
        let (mut spec, _, mut series) = fixture("strength");
        spec.template = "ma_below".into();
        spec.parameters = BTreeMap::from([("ma_window".into(), json!(60))]);
        for bar in &mut series.bars {
            bar.open = Some(10.1);
            bar.high = Some(10.1);
            bar.low = Some(10.1);
            bar.close = Some(10.1);
        }
        let days = series.bars.iter().map(|bar| bar.date).collect::<Vec<_>>();
        let result = evaluate(&spec, &days, &series).unwrap();
        assert!(
            !result.matched,
            "unchanged decimal prices must not trigger a below-MA sell: {:?}",
            result.checks
        );
        assert_eq!(result.values["ma"], 10.1);
    }
    #[test]
    fn daily_patterns_no_missing_data_fallback() {
        let (spec, days, mut series) = fixture("strength");
        series.bars[0].adjustment_factor_backward1 = None;
        assert!(
            evaluate(&spec, &days, &series)
                .unwrap_err()
                .contains("adjustment_factor")
        );
        series.bars[0].paused = Some(true);
        assert!(evaluate(&spec, &days, &series).is_err());
        series.bars[0].adjustment_factor_backward1 = Some(1.0);
        let excluded = evaluate(&spec, &days, &series).unwrap();
        assert!(excluded.exclusion.is_some());
        assert!(!excluded.matched);
        series.bars.remove(0);
        assert!(evaluate(&spec, &days, &series).is_err());
        series.listed_at = Some(days[1]);
        assert!(evaluate(&spec, &days, &series).unwrap().exclusion.is_some());
    }
    #[test]
    fn daily_patterns_rejects_future_and_duplicate_bars() {
        let (spec, days, mut series) = fixture("strength");
        series.bars.push(series.bars[0].clone());
        assert!(evaluate(&spec, &days, &series).is_err());
        series.bars.last_mut().unwrap().date = *days.last().unwrap() + chrono::Duration::days(1);
        assert!(evaluate(&spec, &days, &series).is_err());
    }
    #[test]
    fn daily_patterns_helper_literal_preserves_parameters() {
        let text =
            serde_json::to_string(&json!({"template":"breakout","parameters":{"high_window":252}}))
                .unwrap();
        let expression = format!("pattern_signal({})", serde_json::to_string(&text).unwrap());
        assert_eq!(expression_specs(&expression).unwrap()[0].history_len(), 253);
        assert!(expression_specs("pattern_signal(\"{}\")").is_err());
    }
}