import backtrader as bt
import math
# 1. 網(wǎng)格策略核心類
class GridStrategy(bt.Strategy):
    params = (
        ('number', 10),  # 網(wǎng)格總數(shù)
        ('open_percent', 0.5),  # 初始建倉比例(50%)
        ('distance', 0.01),  # 網(wǎng)格間距(1%)
        ('base_price', 5.0)  # 基準(zhǔn)價格
    )
    def __init__(self):
        self.open_flag = False  # 初始建倉標(biāo)記
        self.last_index = 0  # 上次網(wǎng)格位置
        self.per_size = 0  # 每格交易量
        self.max_index = 0  # 網(wǎng)格上邊界
        self.min_index = 0  # 網(wǎng)格下邊界
    def next(self):
        # 初始建倉邏輯（價格接近基準(zhǔn)價時觸發(fā)）
        if not self.open_flag and math.fabs(self.data.close[0] - self.p.base_price) / self.p.base_price < 0.01:
            # 計算初始倉位（50%資金）
            buy_size = self.broker.getvalue() * self.p.open_percent / self.data.close[0]
            self.buy(size=buy_size)
            # 初始化網(wǎng)格參數(shù)
            self.last_index = 0
            self.per_size = self.broker.getvalue() / self.data.close[0] / self.p.number
            self.max_index = int(self.p.number * self.p.open_percent)
            self.min_index = -int(self.p.number * (1 - self.p.open_percent))
            self.open_flag = True
        # 網(wǎng)格調(diào)倉邏輯
        if self.open_flag:
            # 計算當(dāng)前網(wǎng)格位置（按間距歸一化）
            current_index = int((self.data.close[0] - self.p.base_price) / self.p.distance)
            # 邊界保護(hù)（防止破網(wǎng)）
            current_index = max(min(current_index, self.max_index), self.min_index)
            # 網(wǎng)格變化時交易
            change = current_index - self.last_index
            if change > 0:  # 價格上漲→賣出
                self.sell(size=abs(change) * self.per_size)
            elif change < 0:  # 價格下跌→買入
                self.buy(size=abs(change) * self.per_size)
            self.last_index = current_index  # 更新網(wǎng)格位置
    # 2. 訂單狀態(tài)跟蹤（調(diào)試用）
    def notify_order(self, order):
        if order.status in [order.Completed]:
            action = '買入' if order.isbuy() else '賣出'
            print(f'{action}成交: 價格={order.executed.price:.2f}, 數(shù)量={order.executed.size}')

import backtrader as bt
from GridStrategy import GridStrategy
from backtrader.feeds import PandasData
import ccxt
import pandas as pd
# 連接交易所（以幣安為例）
exchange = ccxt.binance({
    'enableRateLimit': True,  # 啟用頻率限制[6](@ref)
    'options': {'defaultType': 'spot'}  # 現(xiàn)貨數(shù)據(jù)
})
# 獲取ETH/USDT歷史K線（1小時周期）
def fetch_eth_data(days=30, timeframe='1h'):
    since = exchange.parse8601((pd.Timestamp.utcnow() - pd.Timedelta(days=days)).isoformat())
    ohlcv = exchange.fetch_ohlcv('ETH/USDT', timeframe, since=since)
    df = pd.DataFrame(ohlcv, columns=['timestamp', 'open', 'high', 'low', 'close', 'volume'])
    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
    df.set_index('timestamp', inplace=True)
    return df
eth_data = fetch_eth_data(days=90, timeframe='1h')  # 獲取90天1小時數(shù)據(jù)
print(eth_data)
class CCXTData(PandasData):
    lines = ('volume',)  # 聲明額外數(shù)據(jù)列
    params = (
        ('datetime', None),  # 使用索引作為時間列
        ('open', 'open'),
        ('high', 'high'),
        ('low', 'low'),
        ('close', 'close'),
        ('volume', 'volume'),
    )
# 數(shù)據(jù)實例化
data_feed = CCXTData(dataname=eth_data)
# 初始化引擎
cerebro = bt.Cerebro()
cerebro.addstrategy(
    GridStrategy,
    base_price=1803.70,  # 設(shè)置標(biāo)的基準(zhǔn)價
    distance=10,  # 網(wǎng)格間距5%（按價格波動調(diào)整）
    number=100  # 增加網(wǎng)格密度
)
cerebro.adddata(data_feed)
cerebro.broker.setcash(10000.0)  # 初始資金
cerebro.broker.setcommission(commission=0.001)  # 手續(xù)費0.1%[3](@ref)
# 運行回測
print('初始資金:', cerebro.broker.getvalue())
cerebro.run()
print('最終資金:', cerebro.broker.getvalue())
# 可視化結(jié)果
cerebro.plot(style='candlestick')