import akquant as aq
import akshare as ak
from akquant import Strategy

# 1. 准备数据
# 使用 akshare 获取 A 股历史数据 (需安装: pip install akshare)
df = ak.stock_zh_a_daily(symbol="sh600000", start_date="20250212", end_date="20260212")


class MyStrategy(Strategy):
    def on_bar(self, bar):
        # 简单策略示例:
        # 当收盘价 > 开盘价 (阳线) -> 买入
        # 当收盘价 < 开盘价 (阴线) -> 卖出

        # 获取当前持仓
        current_pos = self.get_position(bar.symbol)

        if current_pos == 0 and bar.close > bar.open:
            self.buy(symbol=bar.symbol, quantity=100)
            print(f"[{bar.timestamp_str}] Buy 100 at {bar.close:.2f}")

        elif current_pos > 0 and bar.close < bar.open:
            self.close_position(symbol=bar.symbol)
            print(f"[{bar.timestamp_str}] Sell 100 at {bar.close:.2f}")


# 运行回测
result = aq.run_backtest(
    data=df,
    strategy=MyStrategy,
    initial_cash=100000.0,
    symbols="sh600000"
)

# 打印回测结果
print("\n=== Backtest Result ===")
print(result)

# 生成最小基准对比报告
benchmark_returns = (
    df.set_index("date")["close"].pct_change().fillna(0.0).rename("SIMPLE_BENCH")
)
result.report(
    filename="quickstart_report.html",
    show=False,
    benchmark=benchmark_returns,
)

from akquant import OrderStatus, Strategy

class BracketHelperStrategy(Strategy):
    def __init__(self):
        self.entry_order_id = ""

    def on_bar(self, bar):
        if self.get_position(bar.symbol) > 0 or self.entry_order_id:
            return

        self.entry_order_id = self.place_bracket_order(
            symbol=bar.symbol,
            quantity=100,
            stop_trigger_price=bar.close * 0.98,
            take_profit_price=bar.close * 1.04,
            entry_tag="entry",
            stop_tag="stop",
            take_profit_tag="take",
        )

    def on_order(self, order):
        if order.id == self.entry_order_id and order.status in (
            OrderStatus.Cancelled,
            OrderStatus.Rejected,
        ):
            self.entry_order_id = ""

def on_event(event):
    if event["event_type"] == "finished":
        payload = event["payload"]
        print("status:", payload.get("status"))
        print("callback_error_count:", payload.get("callback_error_count"))

result = aq.run_backtest(
    data=df,
    strategy=MyStrategy,
    symbols="sh600000",
    on_event=on_event,
    show_progress=False,
    stream_progress_interval=10,
    stream_equity_interval=10,
    stream_batch_size=32,
    stream_max_buffer=256,
    stream_error_mode="continue",
)

# 生成交互式 HTML 报告，自动在浏览器中打开
result.report(
    show=True,
    compact_currency=True,  # 金额列按 K/M/B 紧凑显示（默认 True）
)

# 如果你希望金额列保留原始数值精度（不缩写），可关闭：
result.report(
    show=False,
    filename="report_raw_amount.html",
    compact_currency=False,
)

exposure = result.exposure_df()             # 暴露分解（净暴露/总暴露/杠杆）
attr_by_symbol = result.attribution_df(by="symbol")
attr_by_tag = result.attribution_df(by="tag")
capacity = result.capacity_df()             # 容量代理（成交率/换手等）
orders_by_strategy = result.orders_by_strategy()         # 按策略归属聚合订单
executions_by_strategy = result.executions_by_strategy() # 按策略归属聚合成交