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https://github.com/aljazceru/hummingbot-dashboard.git
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303 lines
11 KiB
Python
303 lines
11 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
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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.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['datetime'],
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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['datetime'],
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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['datetime'],
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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['datetime'],
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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['datetime'],
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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['datetime'],
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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.datetime,
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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+text",
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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_net_pnl(self, strategy_data: StrategyData, 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.datetime,
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y=merged_df["net_pnl_continuos"],
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name="Cumulative Net PnL",
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mode="lines+markers+text",
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marker=dict(color="black", size=6),
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line=dict(color="mediumpurple", width=2),
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text=merged_df["net_pnl_continuos"],
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textposition="top center",
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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='Cum Net PnL', row=row, col=1)
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def add_trade_pnl(self, strategy_data: StrategyData, row=5):
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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.datetime,
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y=merged_df["trade_pnl_continuos"],
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name="Cumulative Trade PnL",
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mode="lines+markers+text",
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marker=dict(color="black", size=6),
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line=dict(color="crimson", width=2),
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text=merged_df["trade_pnl_continuos"],
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textposition="top center",
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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='Cum Trade PnL', row=row, col=1)
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def add_trade_fee(self, strategy_data: StrategyData, row=6):
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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.datetime,
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y=merged_df["cum_fees_in_quote"],
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name="Cumulative Fees",
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mode="lines+markers+text",
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marker=dict(color="black", size=6),
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line=dict(color="seagreen", width=2),
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text=merged_df["cum_fees_in_quote"],
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textposition="top center",
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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='Cum Trade Fees', 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_rangeslider_visible=False,
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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_on="datetime", right_on="timestamp", direction="forward")
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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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def get_bar_plot_volume_of_trades(strategy_data: StrategyData):
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grouped_df = strategy_data.trade_fill.groupby("trade_type").agg({"amount": "sum", "order_id": "count"})
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# Create figure with secondary y-axis
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fig = go.Figure()
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fig.add_trace(go.Bar(
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y=['Total Amount'],
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x=grouped_df.loc["BUY", ["amount"]],
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name='Buy Amount',
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orientation='h',
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))
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fig.add_trace(go.Bar(
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y=['Total Amount'],
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x=grouped_df.loc["SELL", ["amount"]],
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name='Sell Amount',
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orientation='h',
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))
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fig.update_layout(template="plotly_white", title="Volume analysis", height=300,
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xaxis_title="Amount in Base Asset")
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return fig
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def get_bar_plot_quantity_of_trades(strategy_data: StrategyData):
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grouped_df = strategy_data.trade_fill.groupby("trade_type").agg({"amount": "sum", "order_id": "count"})
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fig = go.Figure()
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fig.add_trace(go.Bar(
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y=['Quantity of Orders'],
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x=grouped_df.loc["BUY", ["order_id"]],
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name='Quantity of Buys',
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orientation='h',
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))
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fig.add_trace(go.Bar(
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y=['Quantity of Orders'],
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x=grouped_df.loc["SELL", ["order_id"]],
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name='Quantity of Sells',
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orientation='h',
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))
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fig.update_layout(template="plotly_white", title="Excution Analysis", height=300,
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xaxis_title="Quantity of orders")
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return fig
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