Add matching and slippage execution options
This commit is contained in:
@@ -2,7 +2,7 @@ use chrono::NaiveDate;
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use fidc_core::{
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BenchmarkSnapshot, BrokerSimulator, CandidateEligibility, ChinaAShareCostModel,
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ChinaEquityRuleHooks, DailyFactorSnapshot, DailyMarketSnapshot, DataSet, Instrument,
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OrderIntent, PortfolioState, PriceField, StrategyDecision,
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OrderIntent, PortfolioState, PriceField, SlippageModel, StrategyDecision,
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};
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use std::collections::{BTreeMap, BTreeSet};
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@@ -50,6 +50,7 @@ fn broker_executes_explicit_order_value_buy() {
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pe_ttm: 15.0,
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turnover_ratio: Some(2.0),
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effective_turnover_ratio: Some(1.8),
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extra_factors: BTreeMap::new(),
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}],
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vec![CandidateEligibility {
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date,
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@@ -106,6 +107,205 @@ fn broker_executes_explicit_order_value_buy() {
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assert!(portfolio.cash() < 1_000_000.0);
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}
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#[test]
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fn broker_applies_price_ratio_slippage_on_snapshot_fills() {
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let date = NaiveDate::from_ymd_opt(2024, 1, 10).unwrap();
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let data = DataSet::from_components(
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vec![Instrument {
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symbol: "000002.SZ".to_string(),
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name: "Test".to_string(),
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board: "SZ".to_string(),
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round_lot: 100,
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listed_at: None,
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delisted_at: None,
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status: "active".to_string(),
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}],
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vec![DailyMarketSnapshot {
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date,
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symbol: "000002.SZ".to_string(),
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timestamp: Some("2024-01-10 10:18:00".to_string()),
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day_open: 10.0,
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open: 10.0,
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high: 10.1,
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low: 9.9,
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close: 10.0,
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last_price: 10.0,
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bid1: 9.99,
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ask1: 10.01,
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prev_close: 10.0,
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volume: 100_000,
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tick_volume: 100_000,
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bid1_volume: 80_000,
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ask1_volume: 80_000,
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trading_phase: Some("continuous".to_string()),
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paused: false,
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upper_limit: 11.0,
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lower_limit: 9.0,
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price_tick: 0.01,
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}],
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vec![DailyFactorSnapshot {
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date,
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symbol: "000002.SZ".to_string(),
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market_cap_bn: 50.0,
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free_float_cap_bn: 45.0,
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pe_ttm: 15.0,
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turnover_ratio: Some(2.0),
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effective_turnover_ratio: Some(1.8),
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extra_factors: BTreeMap::new(),
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}],
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vec![CandidateEligibility {
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date,
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symbol: "000002.SZ".to_string(),
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is_st: false,
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is_new_listing: false,
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is_paused: false,
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allow_buy: true,
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allow_sell: true,
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is_kcb: false,
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is_one_yuan: false,
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}],
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vec![BenchmarkSnapshot {
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date,
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benchmark: "000300.SH".to_string(),
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open: 100.0,
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close: 100.0,
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prev_close: 99.0,
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volume: 1_000_000,
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}],
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)
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.expect("dataset");
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let mut portfolio = PortfolioState::new(1_000_000.0);
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let broker = BrokerSimulator::new_with_execution_price(
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ChinaAShareCostModel::default(),
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ChinaEquityRuleHooks::default(),
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PriceField::Open,
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)
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.with_slippage_model(SlippageModel::PriceRatio(0.01));
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let report = broker
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.execute(
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date,
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&mut portfolio,
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&data,
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&StrategyDecision {
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rebalance: false,
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target_weights: BTreeMap::new(),
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exit_symbols: BTreeSet::new(),
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order_intents: vec![OrderIntent::Value {
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symbol: "000002.SZ".to_string(),
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value: 100_000.0,
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reason: "price_ratio_slippage".to_string(),
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}],
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notes: Vec::new(),
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diagnostics: Vec::new(),
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},
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)
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.expect("broker execution");
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assert_eq!(report.fill_events.len(), 1);
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assert!((report.fill_events[0].price - 10.1).abs() < 1e-9);
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}
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#[test]
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fn broker_applies_tick_size_slippage_on_intraday_last_fills() {
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let date = NaiveDate::from_ymd_opt(2024, 1, 10).unwrap();
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let data = DataSet::from_components(
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vec![Instrument {
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symbol: "000002.SZ".to_string(),
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name: "Test".to_string(),
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board: "SZ".to_string(),
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round_lot: 100,
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listed_at: None,
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delisted_at: None,
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status: "active".to_string(),
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}],
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vec![DailyMarketSnapshot {
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date,
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symbol: "000002.SZ".to_string(),
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timestamp: Some("2024-01-10 10:18:00".to_string()),
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day_open: 10.0,
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open: 10.0,
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high: 10.1,
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low: 9.9,
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close: 10.0,
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last_price: 10.0,
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bid1: 9.99,
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ask1: 10.01,
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prev_close: 10.0,
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volume: 100_000,
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tick_volume: 100_000,
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bid1_volume: 80_000,
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ask1_volume: 80_000,
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trading_phase: Some("continuous".to_string()),
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paused: false,
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upper_limit: 11.0,
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lower_limit: 9.0,
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price_tick: 0.01,
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}],
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vec![DailyFactorSnapshot {
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date,
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symbol: "000002.SZ".to_string(),
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market_cap_bn: 50.0,
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free_float_cap_bn: 45.0,
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pe_ttm: 15.0,
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turnover_ratio: Some(2.0),
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effective_turnover_ratio: Some(1.8),
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extra_factors: BTreeMap::new(),
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}],
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vec![CandidateEligibility {
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date,
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symbol: "000002.SZ".to_string(),
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is_st: false,
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is_new_listing: false,
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is_paused: false,
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allow_buy: true,
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allow_sell: true,
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is_kcb: false,
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is_one_yuan: false,
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}],
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vec![BenchmarkSnapshot {
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date,
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benchmark: "000300.SH".to_string(),
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open: 100.0,
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close: 100.0,
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prev_close: 99.0,
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volume: 1_000_000,
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}],
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)
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.expect("dataset");
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let mut portfolio = PortfolioState::new(1_000_000.0);
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let broker = BrokerSimulator::new_with_execution_price(
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ChinaAShareCostModel::default(),
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ChinaEquityRuleHooks::default(),
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PriceField::Last,
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)
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.with_intraday_execution_start_time(chrono::NaiveTime::from_hms_opt(10, 18, 0).unwrap())
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.with_slippage_model(SlippageModel::TickSize(2.0));
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let report = broker
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.execute(
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date,
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&mut portfolio,
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&data,
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&StrategyDecision {
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rebalance: false,
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target_weights: BTreeMap::new(),
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exit_symbols: BTreeSet::new(),
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order_intents: vec![OrderIntent::Value {
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symbol: "000002.SZ".to_string(),
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value: 100_000.0,
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reason: "tick_slippage".to_string(),
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}],
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notes: Vec::new(),
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diagnostics: Vec::new(),
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},
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)
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.expect("broker execution");
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assert_eq!(report.fill_events.len(), 1);
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assert!((report.fill_events[0].price - 10.02).abs() < 1e-9);
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}
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#[test]
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fn broker_uses_instrument_round_lot_for_buy_sizing() {
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let date = NaiveDate::from_ymd_opt(2024, 1, 10).unwrap();
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@@ -150,6 +350,7 @@ fn broker_uses_instrument_round_lot_for_buy_sizing() {
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pe_ttm: 20.0,
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turnover_ratio: Some(2.0),
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effective_turnover_ratio: Some(1.8),
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extra_factors: BTreeMap::new(),
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}],
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vec![CandidateEligibility {
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date,
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@@ -256,6 +457,7 @@ fn same_day_sell_then_rebuy_reinserts_position_at_end() {
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pe_ttm: 15.0,
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turnover_ratio: Some(2.0),
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effective_turnover_ratio: Some(1.8),
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extra_factors: BTreeMap::new(),
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})
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.collect::<Vec<_>>();
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let candidates = symbols
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