PowerQuant Backtest

from power_quant_backtest import BacktestEngine

class SingleMaStrategy:
    """最简单的单均线策略：价格上穿 MA20 开多，下穿平多。"""

    def __init__(self, period=20):
        self.period = period
        self.closes = []

    def on_bar(self, bar, context):
        self.closes.append(bar.close)
        if len(self.closes) < self.period:
            return

        ma = sum(self.closes[-self.period:]) / self.period
        symbol = bar.symbol
        long_qty = context.long_quantity(symbol)

        if bar.close > ma and long_qty == 0.0:
            context.buy(symbol, bar.close, 1.0)       # 开多 1 手
        elif bar.close < ma and long_qty > 0.0:
            context.sell(symbol, bar.close, long_qty) # 平多

    def on_start(self, context):
        print("策略启动 | 单均线 MA({})".format(self.period))

    def on_finish(self, context):
        print("策略结束 | 最终资金: {:.2f}".format(context.total_assets))


engine = BacktestEngine(100_000.0)                      # 初始资金 10 万
engine.add_instrument("RB88", 10.0, 0.00001, 1.0, 1.0, 0.1)
#                       ↑合约  ↑乘数  ↑手续费率  ↑滑点  ...
engine.add_bars(bars)                                   # 你的 K 线数据
report = engine.run_with_python_strategy(SingleMaStrategy(period=20))
report.print_summary()

from power_quant_backtest import (
    BacktestEngine, ArrayManager, BarGenerator,
)

class DualMaStrategy:
    """双均线策略：MA(20)/MA(60) 金叉死叉 + 5min 周期合成。"""

    def __init__(self):
        self.bg = BarGenerator.minute_window(5)   # 1min → 5min 合成
        self.am = ArrayManager(100)               # 滑动 OHLCV 数组

    def on_bar(self, bar, context):
        target_bar = self.bg.update(bar)
        if target_bar is None or target_bar.state != 2:
            return                                 # 5min 窗口未闭合

        self.am.update_bar(target_bar)
        if not self.am.inited():
            return                                 # 预热阶段

        closes = self.am.close()
        short_ma = sum(closes[-20:]) / 20
        long_ma = sum(closes[-60:]) / 60
        # ↑ 从 ArrayManager 取出 close 序列计算指标
        # ↓ 金叉开多 / 死叉开空 ...

// examples-rs/dual_ma.rs — Rust Strategy Trait 完整实现模式
// 运行：cargo run --example dual_ma
use power_quant_backtest::{BacktestEngine, Strategy};

pub struct DualMaStrategy {
    short_period: usize,
    long_period: usize,
    closes: Vec<f64>,
}

impl Strategy for DualMaStrategy {
    fn on_bar(&mut self, bar: &Bar, ctx: &mut BacktestContext) {
        self.closes.push(bar.close);
        if self.closes.len() < self.long_period {
            return;
        }
        let short_ma: f64 = self.closes.iter()
            .rev().take(self.short_period).sum::<f64>()
            / self.short_period as f64;
        let long_ma: f64 = self.closes.iter()
            .rev().take(self.long_period).sum::<f64>()
            / self.long_period as f64;
        // ↑ Rust 端直接操作 Vec<f64>，零 Python 互开销
        // ↓ 金叉死叉下单 ...
    }
}

fn main() {
    let mut engine = BacktestEngine::new(100_000.0);
    engine.add_instrument("RB88", 10.0, 0.00001, 1.0, 1.0, 0.1);
    engine.run_with_strategy(DualMaStrategy {
        short_period: 20, long_period: 60, closes: vec![],
    });
}