[英]How to backtest portfolio compositions using backtrader?
我有一個 csv 文件 / pandas dataframe看起來像這樣。 它包含根據我自己的計算每天重新平衡的投資組合的各種投資組合組合。
date asset percentage
4-Jan-21 AAPL 12.00%
4-Jan-21 TSM 1.00%
4-Jan-21 IBM 31.00%
4-Jan-21 KO 15.00%
4-Jan-21 AMD 41.00%
5-Jan-21 DELL 23.00%
5-Jan-21 TSM 12.20%
5-Jan-21 IBM 15.24%
5-Jan-21 KO 1.50%
5-Jan-21 NKE 7.50%
5-Jan-21 TSLA 9.50%
5-Jan-21 CSCO 3.30%
5-Jan-21 JPM 27.76%
6-Jan-21 AMD 45%
6-Jan-21 BA 0.50%
6-Jan-21 ORCL 54.50%
7-Jan-21 AAPL 50.00%
7-Jan-21 KO 50.00%
...
我想測試一個包含 12 個資產組合的策略。
AAPL,TSM,IBM,KO,AMD,DELL,NKE,TSLA,CSCO,JPM,BA,ORCL
因此，假設在 2021 年 1 月 4 日，投資組合的構成將是蘋果的 12%，TSM 的 1%。等等。我希望能夠檢查價格並知道我應該持有多少。
第二天，2021 年 1 月 5 日，戴爾的成分股將變為 23%。等等，如果股票不在此列表中，則表示當天為 0%。
我一直將 backtrader 視為一個回測平台，但是，我在 repo 中看到的代碼主要展示了如何使用指標做一些事情，比如 SMA 交叉、RSI ......
我的問題是：是否可以根據我擁有的這些組合創建和測試投資組合，以便我可以檢查該策略的回報？ 它會檢查這個框架，並知道在特定的一天有多少股票可以買賣。
所以我買賣的股票范圍是AAPL,TSM,IBM,KO,AMD,DELL,NKE,TSLA,CSCO,JPM,BA,ORCL
所以在 21 年 1 月 4 日，它可能看起來像，
dictionary['4Jan2021'] = {'AAPL':0.12,
'TSM':0.01,
'IBM':0.31,
'KO':0.15,
'AMD':0.41,}
在 21 年 1 月 5 日，它看起來像，
dictionary['5Jan2021'] = {'DELL':0.23,
'TSM':0.122,
'IBM':0.1524,
'KO':0.015,
'NKE':0.075,
'TSLA':0.095,
'CSCO':0.033,
'JPM':0.2776,}
如果代碼不存在意味着它的 0%。 投資組合的構成每天都需要改變。
你要做的第一件事就是用你的數據加載你的目標。 當我將目標添加到反向交易者時，我個人喜歡將目標附加到數據線。
tickers = {"FB": 0.25, "MSFT": 0.4, "TSLA": 0.35}
for ticker, target in tickers.items():
data = bt.feeds.YahooFinanceData(
dataname=ticker,
timeframe=bt.TimeFrame.Days,
fromdate=datetime.datetime(2019, 1, 1),
todate=datetime.datetime(2020, 12, 31),
reverse=False,
)
data.target = target
cerebro.adddata(data, name=ticker)
在接下來您將要通過每個數據 go，並確定當前分配。 如果當前分配距離所需分配（閾值）太遠，則交易所有數據。
注意有一個緩沖區變量。 這將降低用於計算要交易的單位的賬戶的總價值。 這有助於避免保證金。
您將使用字典來跟蹤此信息。
def next(self):
track_trades = dict()
total_value = self.broker.get_value() * (1 - self.p.buffer)
for d in self.datas:
track_trades[d] = dict()
value = self.broker.get_value(datas=[d])
allocation = value / total_value
units_to_trade = (d.target - allocation) * total_value / d.close[0]
track_trades[d]["units"] = units_to_trade
# Can check to make sure there is enough distance away from ideal to trade.
track_trades[d]["threshold"] = abs(d.target - allocation) > self.p.threshold
檢查所有閾值以確定是否交易。 如果任何數據需要交易，那么所有數據都需要交易。
rebalance = False
for values in track_trades.values():
if values['threshold']:
rebalance = True
if not rebalance:
return
最后，執行您的交易。 始終首先出售以在賬戶中產生現金並避免利潤。
# Sell shares first
for d, value in track_trades.items():
if value["units"] < 0:
self.sell(d, size=value["units"])
# Buy shares second
for d, value in track_trades.items():
if value["units"] > 0:
self.buy(d, size=value["units"])
這是所有代碼供您參考。
import datetime
import backtrader as bt
class Strategy(bt.Strategy):
params = (
("buffer", 0.05),
("threshold", 0.025),
)
def log(self, txt, dt=None):
""" Logging function fot this strategy"""
dt = dt or self.data.datetime[0]
if isinstance(dt, float):
dt = bt.num2date(dt)
print("%s, %s" % (dt.date(), txt))
def print_signal(self):
self.log(
f"o {self.datas[0].open[0]:7.2f} "
f"h {self.datas[0].high[0]:7.2f} "
f"l {self.datas[0].low[0]:7.2f} "
f"c {self.datas[0].close[0]:7.2f} "
f"v {self.datas[0].volume[0]:7.0f} "
)
def notify_order(self, order):
""" Triggered upon changes to orders. """
# Suppress notification if it is just a submitted order.
if order.status == order.Submitted:
return
# Print out the date, security name, order number and status.
type = "Buy" if order.isbuy() else "Sell"
self.log(
f"{order.data._name:<6} Order: {order.ref:3d} "
f"Type: {type:<5}\tStatus"
f" {order.getstatusname():<8} \t"
f"Size: {order.created.size:9.4f} Price: {order.created.price:9.4f} "
f"Position: {self.getposition(order.data).size:5.2f}"
)
if order.status == order.Margin:
return
# Check if an order has been completed
if order.status in [order.Completed]:
self.log(
f"{order.data._name:<6} {('BUY' if order.isbuy() else 'SELL'):<5} "
# f"EXECUTED for: {dn} "
f"Price: {order.executed.price:6.2f} "
f"Cost: {order.executed.value:6.2f} "
f"Comm: {order.executed.comm:4.2f} "
f"Size: {order.created.size:9.4f} "
)
def notify_trade(self, trade):
"""Provides notification of closed trades."""
if trade.isclosed:
self.log(
"{} Closed: PnL Gross {}, Net {},".format(
trade.data._name,
round(trade.pnl, 2),
round(trade.pnlcomm, 1),
)
)
def next(self):
track_trades = dict()
total_value = self.broker.get_value() * (1 - self.p.buffer)
for d in self.datas:
track_trades[d] = dict()
value = self.broker.get_value(datas=[d])
allocation = value / total_value
units_to_trade = (d.target - allocation) * total_value / d.close[0]
track_trades[d]["units"] = units_to_trade
# Can check to make sure there is enough distance away from ideal to trade.
track_trades[d]["threshold"] = abs(d.target - allocation) > self.p.threshold
rebalance = False
for values in track_trades.values():
if values['threshold']:
rebalance = True
if not rebalance:
return
# Sell shares first
for d, value in track_trades.items():
if value["units"] < 0:
self.sell(d, size=value["units"])
# Buy shares second
for d, value in track_trades.items():
if value["units"] > 0:
self.buy(d, size=value["units"])
if __name__ == "__main__":
cerebro = bt.Cerebro()
tickers = {"FB": 0.25, "MSFT": 0.4, "TSLA": 0.35}
for ticker, target in tickers.items():
data = bt.feeds.YahooFinanceData(
dataname=ticker,
timeframe=bt.TimeFrame.Days,
fromdate=datetime.datetime(2019, 1, 1),
todate=datetime.datetime(2020, 12, 31),
reverse=False,
)
data.target = target
cerebro.adddata(data, name=ticker)
cerebro.addstrategy(Strategy)
# Execute
cerebro.run()
####################################
＃＃＃＃＃＃＃＃＃＃＃＃＃ 編輯 ＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃
####################################
每天增加每只證券的可變分配還有一個額外的要求。 下面的代碼實現了這一點。
import datetime
import backtrader as bt
class Strategy(bt.Strategy):
params = (
("buffer", 0.05),
("threshold", 0.025),
)
def log(self, txt, dt=None):
""" Logging function fot this strategy"""
dt = dt or self.data.datetime[0]
if isinstance(dt, float):
dt = bt.num2date(dt)
print("%s, %s" % (dt.date(), txt))
def print_signal(self):
self.log(
f"o {self.datas[0].open[0]:7.2f} "
f"h {self.datas[0].high[0]:7.2f} "
f"l {self.datas[0].low[0]:7.2f} "
f"c {self.datas[0].close[0]:7.2f} "
f"v {self.datas[0].volume[0]:7.0f} "
)
def notify_order(self, order):
""" Triggered upon changes to orders. """
# Suppress notification if it is just a submitted order.
if order.status == order.Submitted:
return
# Print out the date, security name, order number and status.
type = "Buy" if order.isbuy() else "Sell"
self.log(
f"{order.data._name:<6} Order: {order.ref:3d} "
f"Type: {type:<5}\tStatus"
f" {order.getstatusname():<8} \t"
f"Size: {order.created.size:9.4f} Price: {order.created.price:9.4f} "
f"Position: {self.getposition(order.data).size:5.2f}"
)
if order.status == order.Margin:
return
# Check if an order has been completed
if order.status in [order.Completed]:
self.log(
f"{order.data._name:<6} {('BUY' if order.isbuy() else 'SELL'):<5} "
# f"EXECUTED for: {dn} "
f"Price: {order.executed.price:6.2f} "
f"Cost: {order.executed.value:6.2f} "
f"Comm: {order.executed.comm:4.2f} "
f"Size: {order.created.size:9.4f} "
)
def notify_trade(self, trade):
"""Provides notification of closed trades."""
if trade.isclosed:
self.log(
"{} Closed: PnL Gross {}, Net {},".format(
trade.data._name,
round(trade.pnl, 2),
round(trade.pnlcomm, 1),
)
)
def __init__(self):
for d in self.datas:
d.target = {
datetime.datetime.strptime(date, "%d-%b-%y").date(): allocation
for date, allocation in d.target.items()
}
def next(self):
date = self.data.datetime.date()
track_trades = dict()
total_value = self.broker.get_value() * (1 - self.p.buffer)
for d in self.datas:
if date not in d.target:
if self.getposition(d):
self.close(d)
continue
target_allocation = d.target[date]
track_trades[d] = dict()
value = self.broker.get_value(datas=[d])
current_allocation = value / total_value
net_allocation = target_allocation - current_allocation
units_to_trade = (
(net_allocation) * total_value / d.close[0]
)
track_trades[d]["units"] = units_to_trade
# Can check to make sure there is enough distance away from ideal to trade.
track_trades[d]["threshold"] = abs(net_allocation) > self.p.threshold
rebalance = False
for values in track_trades.values():
if values["threshold"]:
rebalance = True
if not rebalance:
return
# Sell shares first
for d, value in track_trades.items():
if value["units"] < 0:
self.sell(d, size=value["units"])
# Buy shares second
for d, value in track_trades.items():
if value["units"] > 0:
self.buy(d, size=value["units"])
if __name__ == "__main__":
cerebro = bt.Cerebro()
allocations = [
("AAPL", "4-Jan-21", 0.300),
("TSM", "4-Jan-21", 0.200),
("IBM", "4-Jan-21", 0.300),
("KO", "4-Jan-21", 0.2000),
("AMD", "4-Jan-21", 0.1000),
("DELL", "5-Jan-21", 0.200),
("TSM", "5-Jan-21", 0.20),
("IBM", "5-Jan-21", 0.1),
("KO", "5-Jan-21", 0.1),
("NKE", "5-Jan-21", 0.15),
("TSLA", "5-Jan-21", 0.10),
("CSCO", "5-Jan-21", 0.050),
("JPM", "5-Jan-21", 0.1),
("AMD", "6-Jan-21", 0.25),
("BA", "6-Jan-21", 0.25),
("ORCL", "6-Jan-21", 0.50),
("AAPL", "7-Jan-21", 0.5000),
("KO", "7-Jan-21", 0.5000),
]
ticker_names = list(set([alls[0] for alls in allocations]))
targets = {ticker: {} for ticker in ticker_names}
for all in allocations:
targets[all[0]].update({all[1]: all[2]})
for ticker, target in targets.items():
data = bt.feeds.YahooFinanceData(
dataname=ticker,
timeframe=bt.TimeFrame.Days,
fromdate=datetime.datetime(2020, 12, 21),
todate=datetime.datetime(2021, 1, 8),
reverse=False,
)
data.target = target
cerebro.adddata(data, name=ticker)
cerebro.addstrategy(Strategy)
cerebro.broker.setcash(1000000)
# Execute
cerebro.run()
運行 Cerebro 后，我在 backtrader 中的投資組合價值沒有發生任何變化，我如何檢查它是否正常工作
[英]After running Cerebro nothing is happening to my portfolio value in backtrader, how do i check if it is working or not
在 backtrader 上運行回測，我收到了許多訂單取消/保證金/拒絕，而他們不應該這樣做
[英]Running backtest on backtrader I get many BUY ORDERS CANCELLED/MARGIN/REJECTED when they should not
如何使用帶有本地 csv 文件的 Backtrader 和 Python 解決回測的數據饋送錯誤？
[英]How to resolve datafeeds error for backtesting using Backtrader and Python with local csv file?
如何在 streamlit 中顯示 backtrader 返回的圖表？
[英]How I display the graph that return by backtrader in streamlit?
如何在 CCXTStore 中添加多個時間范圍？ [Python，后台交易]
[英]How to add multiple timeframes in CCXTStore? [Python, backtrader]
聲明:本站的技術帖子網頁，遵循CC BY-SA 4.0協議，如果您需要轉載，請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.