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342 lines
14 KiB
Python
342 lines
14 KiB
Python
import pandas as pd
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from plotly.subplots import make_subplots
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import pandas_ta as ta # noqa: F401
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import streamlit as st
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from utils.data_manipulation import StrategyData, SingleMarketStrategyData
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from quants_lab.strategy.strategy_analysis import StrategyAnalysis
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import plotly.graph_objs as go
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class CandlesGraph:
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def __init__(self, candles_df: pd.DataFrame, show_volume=True, extra_rows=1):
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self.candles_df = candles_df
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self.show_volume = show_volume
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rows, heights = self.get_n_rows_and_heights(extra_rows)
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self.rows = rows
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specs = [[{"secondary_y": True}]] * rows
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self.base_figure = make_subplots(rows=rows, cols=1, shared_xaxes=True, vertical_spacing=0.005,
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row_heights=heights, specs=specs)
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self.min_time = candles_df.reset_index().timestamp.min()
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self.max_time = candles_df.reset_index().timestamp.max()
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self.add_candles_graph()
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if self.show_volume:
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self.add_volume()
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self.update_layout()
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def get_n_rows_and_heights(self, extra_rows):
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rows = 1 + extra_rows + self.show_volume
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row_heights = [0.4] * (extra_rows)
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if self.show_volume:
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row_heights.insert(0, 0.05)
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row_heights.insert(0, 0.8)
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return rows, row_heights
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def figure(self):
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return self.base_figure
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def add_candles_graph(self):
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self.base_figure.add_trace(
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go.Candlestick(
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x=self.candles_df.index,
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open=self.candles_df['open'],
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high=self.candles_df['high'],
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low=self.candles_df['low'],
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close=self.candles_df['close'],
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name="OHLC"
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),
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row=1, col=1,
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)
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def add_buy_trades(self, orders_data: pd.DataFrame):
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self.base_figure.add_trace(
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go.Scatter(
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x=orders_data['timestamp'],
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y=orders_data['price'],
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name='Buy Orders',
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mode='markers',
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marker=dict(
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symbol='triangle-up',
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color='green',
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size=12,
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line=dict(color='black', width=1),
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opacity=0.7,
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)),
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row=1, col=1,
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)
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def add_sell_trades(self, orders_data: pd.DataFrame):
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self.base_figure.add_trace(
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go.Scatter(
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x=orders_data['timestamp'],
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y=orders_data['price'],
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name='Sell Orders',
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mode='markers',
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marker=dict(symbol='triangle-down',
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color='red',
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size=12,
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line=dict(color='black', width=1),
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opacity=0.7, )),
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row=1, col=1,
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)
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def add_bollinger_bands(self, length=20, std=2.0, row=1):
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df = self.candles_df.copy()
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if len(df) < length:
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st.warning("Not enough data to calculate Bollinger Bands")
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return
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df.ta.bbands(length=length, std=std, append=True)
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self.base_figure.add_trace(
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go.Scatter(
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x=df.index,
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y=df[f'BBU_{length}_{std}'],
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name='Bollinger Bands',
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mode='lines',
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line=dict(color='blue', width=1)),
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row=row, col=1,
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)
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self.base_figure.add_trace(
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go.Scatter(
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x=df.index,
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y=df[f'BBM_{length}_{std}'],
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name='Bollinger Bands',
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mode='lines',
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line=dict(color='blue', width=1)),
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row=1, col=1,
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)
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self.base_figure.add_trace(
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go.Scatter(
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x=df.index,
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y=df[f'BBL_{length}_{std}'],
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name='Bollinger Bands',
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mode='lines',
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line=dict(color='blue', width=1)),
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row=1, col=1,
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)
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def add_volume(self):
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self.base_figure.add_trace(
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go.Bar(
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x=self.candles_df.index,
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y=self.candles_df['volume'],
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name="Volume",
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opacity=0.5,
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marker=dict(color='lightgreen'),
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),
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row=2, col=1,
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)
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def add_ema(self, length=20, row=1):
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df = self.candles_df.copy()
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if len(df) < length:
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st.warning("Not enough data to calculate EMA")
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return
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df.ta.ema(length=length, append=True)
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self.base_figure.add_trace(
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go.Scatter(
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x=df.index,
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y=df[f'EMA_{length}'],
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name='EMA',
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mode='lines',
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line=dict(color='yellow', width=1)),
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row=row, col=1,
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)
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def add_base_inventory_change(self, strategy_data: StrategyData, row=3):
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# Create a list of colors based on the sign of the amount_new column
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self.base_figure.add_trace(
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go.Bar(
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x=strategy_data.trade_fill["timestamp"],
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y=strategy_data.trade_fill["net_amount"],
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name="Base Inventory Change",
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opacity=0.5,
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marker=dict(color=["lightgreen" if amount > 0 else "indianred" for amount in
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strategy_data.trade_fill["net_amount"]])
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),
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row=row, col=1,
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)
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# TODO: Review impact in different subgraphs
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merged_df = self.get_merged_df(strategy_data)
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self.base_figure.add_trace(
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go.Scatter(
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x=merged_df.index,
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y=merged_df["cum_net_amount"],
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name="Cumulative Base Inventory Change",
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mode="lines+markers",
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marker=dict(color="black", size=6),
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line=dict(color="royalblue", width=2),
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# text=merged_df["cum_net_amount"],
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# textposition="top center",
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# texttemplate="%{text:.2f}"
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),
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row=row, col=1
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)
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self.base_figure.update_yaxes(title_text='Base Inventory Change', row=row, col=1)
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def add_pnl(self, strategy_data: SingleMarketStrategyData, row=4):
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merged_df = self.get_merged_df(strategy_data)
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self.base_figure.add_trace(
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go.Scatter(
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x=merged_df.index,
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y=merged_df["cum_fees_in_quote"].apply(lambda x: round(-x, 2)),
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name="Cum Fees",
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mode='lines',
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line_color='teal',
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fill="tozeroy", # Fill to the line below (trade pnl)
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stackgroup='one'
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),
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row=row, col=1
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)
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self.base_figure.add_trace(
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go.Scatter(
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x=merged_df.index,
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y=merged_df["trade_pnl_continuos"].apply(lambda x: round(x, 2)),
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name="Cum Trade PnL",
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mode='lines',
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line_color='pink',
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fill="tonexty", # Fill to the line below (net pnl)
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stackgroup='one'
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),
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row=row, col=1
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)
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self.base_figure.add_trace(
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go.Scatter(
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x=merged_df.index,
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y=merged_df["net_pnl_continuos"].apply(lambda x: round(x, 2)),
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name="Cum Net PnL",
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mode="lines+markers",
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marker=dict(color="black", size=6),
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line=dict(color="black", width=2),
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# textposition="top center",
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# text=merged_df["net_pnl_continuos"],
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# texttemplate="%{text:.1f}"
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),
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row=row, col=1
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)
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self.base_figure.update_yaxes(title_text='PNL', row=row, col=1)
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def update_layout(self):
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self.base_figure.update_layout(
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title={
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'text': "Market activity",
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'y': 0.95,
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'x': 0.5,
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'xanchor': 'center',
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'yanchor': 'top'
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},
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=-0.2,
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xanchor="right",
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x=1
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),
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height=1500,
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xaxis=dict(rangeslider_visible=False,
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range=[self.min_time, self.max_time]),
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yaxis=dict(range=[self.candles_df.low.min(), self.candles_df.high.max()]),
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hovermode='x unified'
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)
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self.base_figure.update_yaxes(title_text="Price", row=1, col=1)
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if self.show_volume:
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self.base_figure.update_yaxes(title_text="Volume", row=2, col=1)
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self.base_figure.update_xaxes(title_text="Time", row=self.rows, col=1)
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def get_merged_df(self, strategy_data: StrategyData):
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merged_df = pd.merge_asof(self.candles_df, strategy_data.trade_fill, left_index=True, right_on="timestamp", direction="backward")
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merged_df["trade_pnl_continuos"] = merged_df["unrealized_trade_pnl"] + merged_df["cum_net_amount"] * merged_df["close"]
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merged_df["net_pnl_continuos"] = merged_df["trade_pnl_continuos"] - merged_df["cum_fees_in_quote"]
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return merged_df
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class BacktestingGraphs:
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def __init__(self, study_df: pd.DataFrame):
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self.study_df = study_df
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def pnl_vs_maxdrawdown(self):
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fig = go.Figure()
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fig.add_trace(go.Scatter(name="Pnl vs Max Drawdown",
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x=-100 * self.study_df["max_drawdown_pct"],
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y=100 * self.study_df["net_profit_pct"],
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mode="markers",
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text=None,
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hovertext=self.study_df["hover_text"]))
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fig.update_layout(
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title="PnL vs Max Drawdown",
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xaxis_title="Max Drawdown [%]",
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yaxis_title="Net Profit [%]",
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height=800
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)
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fig.data[0].text = []
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return fig
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@staticmethod
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def get_trial_metrics(strategy_analysis: StrategyAnalysis):
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"""Isolated method because it needs to be called from analyze and simulate pages"""
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metrics_container = st.container()
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with metrics_container:
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col1, col2 = st.columns(2)
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with col1:
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st.subheader("🏦 Market")
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with col2:
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st.subheader("📋 General stats")
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col1, col2, col3, col4 = st.columns(4)
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with col1:
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st.metric("Exchange", st.session_state["strategy_params"]["exchange"])
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with col2:
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st.metric("Trading Pair", st.session_state["strategy_params"]["trading_pair"])
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with col3:
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st.metric("Start date", strategy_analysis.start_date().strftime("%Y-%m-%d %H:%M"))
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st.metric("End date", strategy_analysis.end_date().strftime("%Y-%m-%d %H:%M"))
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with col4:
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st.metric("Duration (hours)", f"{strategy_analysis.duration_in_minutes() / 60:.2f}")
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st.metric("Price change", st.session_state["strategy_params"]["trading_pair"])
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st.subheader("📈 Performance")
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col1, col2, col3, col4, col5, col6, col7, col8 = st.columns(8)
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with col1:
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st.metric("Net PnL USD",
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f"{strategy_analysis.net_profit_usd():.2f}",
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delta=f"{100 * strategy_analysis.net_profit_pct():.2f}%",
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help="The overall profit or loss achieved.")
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with col2:
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st.metric("Total positions",
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f"{strategy_analysis.total_positions()}",
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help="The total number of closed trades, winning and losing.")
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with col3:
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st.metric("Accuracy",
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f"{100 * (len(strategy_analysis.win_signals()) / strategy_analysis.total_positions()):.2f} %",
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help="The percentage of winning trades, the number of winning trades divided by the"
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" total number of closed trades")
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with col4:
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st.metric("Profit factor",
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f"{strategy_analysis.profit_factor():.2f}",
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help="The amount of money the strategy made for every unit of money it lost, "
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"gross profits divided by gross losses.")
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with col5:
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st.metric("Max Drawdown",
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f"{strategy_analysis.max_drawdown_usd():.2f}",
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delta=f"{100 * strategy_analysis.max_drawdown_pct():.2f}%",
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help="The greatest loss drawdown, i.e., the greatest possible loss the strategy had compared "
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"to its highest profits")
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with col6:
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st.metric("Avg Profit",
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f"{strategy_analysis.avg_profit():.2f}",
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help="The sum of money gained or lost by the average trade, Net Profit divided by "
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"the overall number of closed trades.")
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with col7:
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st.metric("Avg Minutes",
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f"{strategy_analysis.avg_trading_time_in_minutes():.2f}",
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help="The average number of minutes that elapsed during trades for all closed trades.")
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with col8:
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st.metric("Sharpe Ratio",
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f"{strategy_analysis.sharpe_ratio():.2f}",
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help="The Sharpe ratio is a measure that quantifies the risk-adjusted return of an investment"
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" or portfolio. It compares the excess return earned above a risk-free rate per unit of"
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" risk taken.")
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st.plotly_chart(strategy_analysis.pnl_over_time(), use_container_width=True)
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strategy_analysis.create_base_figure(volume=add_volume, positions=add_positions, trade_pnl=add_pnl)
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st.plotly_chart(strategy_analysis.figure(), use_container_width=True)
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return metrics_container |