绘制回测资产曲线和指标图。
示例:
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MetricsGraph
¶
Source code in omicron/plotting/metrics.py
class MetricsGraph:
def __init__(
self, bills: dict, metrics: dict, baseline_code: Optional[str] = "399300.XSHE"
):
self.metrics = metrics
self.trades = bills["trades"]
self.positions = bills["positions"]
self.start = arrow.get(bills["assets"][0][0]).date()
self.end = arrow.get(bills["assets"][-1][0]).date()
self.frames = [
tf.int2date(f) for f in tf.get_frames(self.start, self.end, FrameType.DAY)
]
# 记录日期到下标的反向映射，这对于在不o
self._frame2pos = {f: i for i, f in enumerate(self.frames)}
self.ticks = self._format_tick(self.frames)
self.assets = pd.DataFrame(bills["assets"], columns=["frame", "assets"])[
"assets"
].to_numpy()
self.nv = self.assets / self.assets[0]
self.baseline_code = baseline_code or "399300.XSHE"
def _fill_missing_prices(self, bars: BarsArray, frames: Union[List, NDArray]):
"""将bars中缺失值采用其前值替换
当baseline为个股时，可能存在停牌的情况，这样导致由此计算的参考收益无法与回测的资产收益对齐，因此需要进行调整。
出于这个目的，本函数只返回处理后的收盘价。
Args:
bars: 基线行情数据。
frames: 日期索引
Returns:
补充缺失值后的收盘价序列
"""
_close = pd.DataFrame(
{
"close": pd.Series(bars["close"], index=bars["frame"]),
"frame": pd.Series(np.empty((len(frames),)), index=frames),
}
)["close"].to_numpy()
# 这里使用omicron中的fill_nan，是因为如果数组的第一个元素即为NaN的话，那么DataFrame.fillna(method='ffill')将无法处理这样的情况(仍然保持为nan)
return fill_nan(_close)
def _format_tick(self, frames: Union[Frame, List[Frame]]) -> Union[str, NDArray]:
if type(frames) == datetime.date:
x = frames
return f"{x.year:02}-{x.month:02}-{x.day:02}"
elif type(frames) == datetime.datetime:
x = frames
return f"{x.month:02}-{x.day:02} {x.hour:02}:{x.minute:02}"
elif type(frames[0]) == datetime.date: # type: ignore
return np.array([f"{x.year:02}-{x.month:02}-{x.day:02}" for x in frames])
else:
return np.array(
[f"{x.month:02}-{x.day:02} {x.hour:02}:{x.minute:02}" for x in frames] # type: ignore
)
async def _metrics_trace(self):
metric_names = {
"start": "起始日",
"end": "结束日",
"window": "资产暴露窗口",
"total_tx": "交易次数",
"total_profit": "总利润",
"total_profit_rate": "利润率",
"win_rate": "胜率",
"mean_return": "日均回报",
"sharpe": "夏普率",
"max_drawdown": "最大回撤",
"annual_return": "年化回报",
"volatility": "波动率",
"sortino": "sortino",
"calmar": "calmar",
}
# bug: plotly go.Table.Cells format not work here
metric_formatter = {
"start": "{}",
"end": "{}",
"window": "{}",
"total_tx": "{}",
"total_profit": "{:.2f}",
"total_profit_rate": "{:.2%}",
"win_rate": "{:.2%}",
"mean_return": "{:.2%}",
"sharpe": "{:.2f}",
"max_drawdown": "{:.2%}",
"annual_return": "{:.2%}",
"volatility": "{:.2%}",
"sortino": "{:.2f}",
"calmar": "{:.2f}",
}
metrics = deepcopy(self.metrics)
baseline = metrics["baseline"] or {}
del metrics["baseline"]
baseline_name = (
await Security.alias(self.baseline_code) if self.baseline_code else "基准"
)
metrics_formatted = []
for k in metric_names.keys():
if metrics.get(k):
metrics_formatted.append(metric_formatter[k].format(metrics.get(k)))
else:
metrics_formatted.append("-")
baseline_formatted = []
for k in metric_names.keys():
if baseline.get(k):
baseline_formatted.append(metric_formatter[k].format(baseline.get(k)))
else:
baseline_formatted.append("-")
return go.Table(
header=dict(values=["指标名", "策略", baseline_name]),
cells=dict(
values=[
[v for _, v in metric_names.items()],
metrics_formatted,
baseline_formatted,
],
font_size=10,
),
)
async def _trade_info_trace(self):
"""构建hover text 序列"""
# convert trades into hover_info
buys = defaultdict(list)
sells = defaultdict(list)
for _, trade in self.trades.items():
trade_date = arrow.get(trade["time"]).date()
ipos = self._frame2pos.get(trade_date)
if ipos is None:
logger.warning(
"date %s in trade record not in backtest range", trade_date
)
continue
name = await Security.alias(trade["security"])
price = trade["price"]
side = trade["order_side"]
filled = trade["filled"]
trade_text = f"{side}:{name} {filled/100:.0f}手 价格:{price:.02f} 成交额{filled * price/10000:.1f}万"
if side == "卖出":
sells[trade_date].append(trade_text)
elif side in ("买入", "分红配股"):
buys[trade_date].append(trade_text)
X_buy, Y_buy, data_buy = [], [], []
X_sell, Y_sell, data_sell = [], [], []
for dt, text in buys.items():
ipos = self._frame2pos.get(dt)
Y_buy.append(self.nv[ipos])
X_buy.append(self._format_tick(dt))
asset = self.assets[ipos]
hover = f"资产:{asset/10000:.1f}万<br>{'<br>'.join(text)}"
data_buy.append(hover)
trace_buy = go.Scatter(
x=X_buy,
y=Y_buy,
mode="markers",
text=data_buy,
name="买入成交",
marker=dict(color="red", symbol="triangle-up"),
)
for dt, text in sells.items():
ipos = self._frame2pos.get(dt)
Y_sell.append(self.nv[ipos])
X_sell.append(self._format_tick(dt))
asset = self.assets[ipos]
hover = f"资产:{asset/10000:.1f}万<br>{'<br>'.join(text)}"
data_sell.append(hover)
trace_sell = go.Scatter(
x=X_sell,
y=Y_sell,
mode="markers",
text=data_sell,
name="卖出成交",
marker=dict(color="green", symbol="triangle-down"),
)
return trace_buy, trace_sell
async def plot(self):
"""绘制资产曲线及回测指标图"""
n = len(self.assets)
bars = await Stock.get_bars(self.baseline_code, n, FrameType.DAY, self.end)
baseline_prices = self._fill_missing_prices(bars, self.frames)
baseline_prices /= baseline_prices[0]
fig = make_subplots(
rows=1,
cols=2,
shared_xaxes=False,
specs=[
[{"type": "scatter"}, {"type": "table"}],
],
column_width=[0.75, 0.25],
horizontal_spacing=0.01,
subplot_titles=("资产曲线", "策略指标"),
)
fig.add_trace(await self._metrics_trace(), row=1, col=2)
print("baseline", len(baseline_prices))
baseline_trace = go.Scatter(
y=baseline_prices,
x=self.ticks,
mode="lines",
name="baseline",
showlegend=True,
)
fig.add_trace(baseline_trace, row=1, col=1)
nv_trace = go.Scatter(
y=self.nv, x=self.ticks, mode="lines", name="策略净值", showlegend=True
)
fig.add_trace(nv_trace, row=1, col=1)
for trace in await self._trade_info_trace():
fig.add_trace(trace, row=1, col=1)
fig.update_xaxes(type="category", tickangle=45, nticks=len(self.ticks) // 5)
fig.update_layout(margin=dict(l=20, r=20, t=50, b=50), width=1040, height=435)
fig.show()
plot(self)
async
¶
绘制资产曲线及回测指标图
Source code in omicron/plotting/metrics.py
async def plot(self):
"""绘制资产曲线及回测指标图"""
n = len(self.assets)
bars = await Stock.get_bars(self.baseline_code, n, FrameType.DAY, self.end)
baseline_prices = self._fill_missing_prices(bars, self.frames)
baseline_prices /= baseline_prices[0]
fig = make_subplots(
rows=1,
cols=2,
shared_xaxes=False,
specs=[
[{"type": "scatter"}, {"type": "table"}],
],
column_width=[0.75, 0.25],
horizontal_spacing=0.01,
subplot_titles=("资产曲线", "策略指标"),
)
fig.add_trace(await self._metrics_trace(), row=1, col=2)
print("baseline", len(baseline_prices))
baseline_trace = go.Scatter(
y=baseline_prices,
x=self.ticks,
mode="lines",
name="baseline",
showlegend=True,
)
fig.add_trace(baseline_trace, row=1, col=1)
nv_trace = go.Scatter(
y=self.nv, x=self.ticks, mode="lines", name="策略净值", showlegend=True
)
fig.add_trace(nv_trace, row=1, col=1)
for trace in await self._trade_info_trace():
fig.add_trace(trace, row=1, col=1)
fig.update_xaxes(type="category", tickangle=45, nticks=len(self.ticks) // 5)
fig.update_layout(margin=dict(l=20, r=20, t=50, b=50), width=1040, height=435)
fig.show()