mirror of
https://github.com/awwesomeman/vectorbt-visualization.git
synced 2026-07-27 18:57:46 +00:00
tsunglin
419 lines
15 KiB
Python
419 lines
15 KiB
Python
import numpy as np
|
|
import pandas as pd
|
|
import matplotlib.pyplot as plt
|
|
import plotly.graph_objects as go
|
|
import shioaji as sj
|
|
from tabulate import tabulate
|
|
import yfinance as yf
|
|
import warnings
|
|
warnings.simplefilter(action='ignore', category=FutureWarning)
|
|
warnings.filterwarnings("ignore")
|
|
|
|
api = sj.Shioaji()
|
|
api.login("id_num", "password")
|
|
api.activate_ca(
|
|
ca_path="C:\\Users\\tsunglin\\Desktop\\SinoPac.pfx",
|
|
ca_passwd="id_num",
|
|
person_id="id_num")
|
|
|
|
class Plot():
|
|
def __init__(self,api=api):
|
|
self.api = api
|
|
|
|
def input_stock(self,kbars_df,stock_id,freq='B'):
|
|
|
|
if freq =='M':
|
|
label = 'left'
|
|
else:
|
|
label = 'right'
|
|
|
|
data = {
|
|
"Open":kbars_df["Open"].resample(freq,closed='right',label=label).first(),
|
|
"High":kbars_df["High"].resample(freq,closed='right',label=label).max(),
|
|
"Low":kbars_df["Low"].resample(freq,closed='right',label=label).min(),
|
|
"Close":kbars_df["Close"].resample(freq,closed='right',label=label).last(),
|
|
"Volume":kbars_df["Volume"].resample(freq,closed='right',label=label).sum()
|
|
}
|
|
kbars_df = pd.DataFrame(data,index=data['Open'].index).dropna()
|
|
self.freq = freq
|
|
self.stock_id = stock_id
|
|
self.kbars_df = kbars_df
|
|
|
|
def get_stock(self,stock_id,start,end,freq='B'):
|
|
'''
|
|
freq = Day -> 'B',
|
|
Minutes -> '10T',
|
|
Week -> 'W',
|
|
Month -> 'M', set label = 'left'
|
|
'''
|
|
|
|
if freq =='M':
|
|
label = 'left'
|
|
else:
|
|
label = 'right'
|
|
|
|
contract = self.api.Contracts.Stocks[stock_id]
|
|
kbars = self.api.kbars(contract, start=start, end=end)
|
|
kbars_df = pd.DataFrame({**kbars})
|
|
kbars_df.ts = pd.to_datetime(kbars_df.ts)
|
|
kbars_df = kbars_df.set_index('ts')
|
|
data = {
|
|
"Open":kbars_df["Open"].resample(freq,closed='right',label=label).first(),
|
|
"High":kbars_df["High"].resample(freq,closed='right',label=label).max(),
|
|
"Low":kbars_df["Low"].resample(freq,closed='right',label=label).min(),
|
|
"Close":kbars_df["Close"].resample(freq,closed='right',label=label).last(),
|
|
"Volume":kbars_df["Volume"].resample(freq,closed='right',label=label).sum()
|
|
}
|
|
kbars_df = pd.DataFrame(data,index=data['Open'].index).dropna()
|
|
kbars_df.index.name="Date"
|
|
|
|
self.freq = freq
|
|
self.stock_id = stock_id
|
|
self.kbars_df = kbars_df
|
|
|
|
def __strategy_type(self):
|
|
data = self.kbars_df
|
|
trading_type = self.trading_type
|
|
init_buy_sig = self.init_buy_sig
|
|
init_sell_sig = self.init_sell_sig
|
|
|
|
if trading_type == 'standard':
|
|
|
|
buy_sig=init_buy_sig.copy()
|
|
buy_sig[init_buy_sig]=1
|
|
buy_sig[init_sell_sig]=-1
|
|
strategy_sig = buy_sig.replace(0,np.nan).ffill()
|
|
|
|
strategy_buy = strategy_sig[strategy_sig.diff()==2]
|
|
strategy_sell = strategy_sig[strategy_sig.diff()==-2]
|
|
|
|
strategy_buy_mark = data[data.index.isin(strategy_buy.index)]
|
|
strategy_buy_mark['Signal'] = 1
|
|
strategy_sell_mark = data[data.index.isin(strategy_sell.index)]
|
|
strategy_sell_mark['Signal'] = 0
|
|
|
|
if trading_type =='customize':
|
|
pass
|
|
|
|
return strategy_sell_mark, strategy_buy_mark
|
|
|
|
def run(self,enter_comm=0.001425*0.28,exit_comm=0.001425*0.28+0.003,buy_at_kbar="Close", sell_at_kbar="Close"):
|
|
|
|
sell_mark, buy_mark = self.__strategy_type()
|
|
self.buy_mark = buy_mark
|
|
self.sell_mark = sell_mark
|
|
self.buy_at_kbar = buy_at_kbar
|
|
self.sell_at_kbar = sell_at_kbar
|
|
|
|
df = pd.concat([buy_mark, sell_mark],join='outer',sort=False).sort_index()
|
|
df['Holding periods'] = df.index.to_series().diff().shift(-1)
|
|
df['Profit'] = (df[sell_at_kbar].shift(-1)*(1-exit_comm) - df[buy_at_kbar]*(1+enter_comm)) / (df[buy_at_kbar]*(1+enter_comm))
|
|
df['Absolute value'] = df['Profit'].abs()
|
|
df['Gain / Loss'] = df['Profit']>0
|
|
df['Gain / Loss'] = df['Gain / Loss'].map({True:'$Gain$',False:'Loss'})
|
|
|
|
profit = df[df['Signal'] == 1][['Profit','Holding periods','Absolute value','Gain / Loss']]
|
|
profit = profit.dropna()
|
|
profit['Cumulative ret'] = (profit['Profit']+1).cumprod()
|
|
profit['Maximum draw drown'] = ((profit['Cumulative ret'] - profit['Cumulative ret'].cummax())/profit['Cumulative ret'].cummax())
|
|
|
|
|
|
average = (profit['Profit'].mean()) * 100
|
|
std = profit['Profit'].std()
|
|
win_rate = ((profit['Profit']>0).sum() / len(profit))*100
|
|
average_period = profit['Holding periods'].mean()
|
|
num_trade = len(profit)
|
|
data = {"Average return(%)":average,"Strategy std":std,"Number of trades":num_trade,"Winning rate(%)":win_rate,'Average holding periods':average_period}
|
|
strategy_info = pd.DataFrame(data,index=['Strategy info'])
|
|
|
|
profit[['Profit','Absolute value','Cumulative ret','Maximum draw drown']] = (profit[['Profit','Absolute value','Cumulative ret','Maximum draw drown']]).apply(lambda x:round(x,4))
|
|
strategy_info[['Average return(%)','Strategy std','Winning rate(%)']] = (strategy_info[['Average return(%)','Strategy std','Winning rate(%)']]).apply(lambda x:round(x,4))
|
|
print(tabulate(profit,headers=profit.columns, tablefmt='pretty'))
|
|
print(tabulate(strategy_info,headers=strategy_info.columns, tablefmt='pretty'))
|
|
|
|
self.trading_detail = df
|
|
self.profit_detail = profit
|
|
self.strategy_info = strategy_info
|
|
|
|
def plot(self):
|
|
|
|
df = self.kbars_df
|
|
freq = self.freq
|
|
stock_id = self.stock_id
|
|
indicator_set = self.indicators
|
|
buy_mark = self.buy_mark
|
|
sell_mark = self.sell_mark
|
|
profit_detail = self.profit_detail
|
|
buy_at_kbar = self.buy_at_kbar
|
|
sell_at_kbar = self.buy_at_kbar
|
|
#----------------------------------------------------------------------------------
|
|
# create plot
|
|
#----------------------------------------------------------------------------------
|
|
# candle stick, buy and sell signals (y6)
|
|
#----------------------------------------------------------------------------------
|
|
fig = go.Figure()
|
|
|
|
fig.add_trace(
|
|
go.Candlestick(x=df.index,
|
|
open=df['Open'],
|
|
high=df['High'],
|
|
low=df['Low'],
|
|
close=df ['Close'],
|
|
name="Stock Prices",
|
|
increasing={'line': {'color': '#DC3912'}},
|
|
decreasing={'line': {'color': '#222A2A'}},
|
|
yaxis='y6' ))
|
|
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=buy_mark.index,
|
|
y=buy_mark[buy_at_kbar],
|
|
name="Buy Signal",
|
|
marker=dict(color="#0DF9FF", size=15),
|
|
mode="markers",
|
|
marker_symbol="triangle-up",
|
|
yaxis='y6'))
|
|
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=sell_mark.index,
|
|
y=sell_mark[sell_at_kbar],
|
|
name="Sell Signal",
|
|
marker=dict(color="#778AAE", size=15),
|
|
mode="markers",
|
|
marker_symbol="triangle-down",
|
|
yaxis='y6'))
|
|
#----------------------------------------------------------------------------------
|
|
# technical indicator (y5 or y6)
|
|
#----------------------------------------------------------------------------------
|
|
|
|
if indicator_set:
|
|
for indicators in indicator_set:
|
|
|
|
if indicators[0]=="main":
|
|
|
|
for indicator_name, indicator in indicators[1].items():
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=indicator.index,
|
|
y=indicator,
|
|
name=indicator_name,
|
|
yaxis='y6'))
|
|
|
|
if indicators[0]=="sub":
|
|
|
|
for indicator_name, indicator in indicators[1].items():
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=indicator.index,
|
|
y=indicator,
|
|
name=indicator_name,
|
|
yaxis='y5'))
|
|
|
|
#----------------------------------------------------------------------------------
|
|
# volume (y4)
|
|
#----------------------------------------------------------------------------------
|
|
fig.add_trace(
|
|
go.Bar(
|
|
x=df.index,
|
|
y=df['Volume'],
|
|
yaxis='y4',
|
|
name='Volume',
|
|
marker_color='#795548'))
|
|
|
|
#----------------------------------------------------------------------------------
|
|
# individual profit plot (y3)
|
|
#----------------------------------------------------------------------------------
|
|
|
|
|
|
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=profit_detail.index,
|
|
y=profit_detail.Profit*100,
|
|
marker_size = (profit_detail["Absolute value"])*100,
|
|
marker_color = profit_detail["Gain / Loss"].map({'$Gain$':'#D62728',"Loss":'#2CA02C'}),
|
|
|
|
marker_symbol='circle',
|
|
mode='markers',
|
|
yaxis='y3',
|
|
name = 'Gain/Loss for each trade(%)'))
|
|
|
|
|
|
|
|
#----------------------------------------------------------------------------------
|
|
# cumulative profit plot (y2)
|
|
#----------------------------------------------------------------------------------
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=profit_detail.index,
|
|
y=profit_detail['Cumulative ret']*100,
|
|
name='Cumulative return(%)',
|
|
line_color='#3366CC',
|
|
yaxis='y2'))
|
|
#----------------------------------------------------------------------------------
|
|
# maximum drawdown (y)
|
|
#----------------------------------------------------------------------------------
|
|
|
|
fig.add_trace(
|
|
go.Scatter(
|
|
x=profit_detail.index,
|
|
y=profit_detail['Maximum draw drown']*100,
|
|
name='Maximum draw down(%)',
|
|
line_color='#DC3912',
|
|
yaxis='y'))
|
|
|
|
|
|
#----------------------------------------------------------------------------------
|
|
# setup layout
|
|
#----------------------------------------------------------------------------------
|
|
# y axis
|
|
#----------------------------------------------------------------------------------
|
|
fig.update_layout(
|
|
xaxis=dict(
|
|
autorange=True,
|
|
rangeslider=dict(
|
|
autorange=True,
|
|
),
|
|
type="date"
|
|
),
|
|
yaxis=dict(
|
|
anchor="x",
|
|
autorange=True,
|
|
domain=[0, 0.1],
|
|
linecolor="#DC3912",
|
|
mirror=True,
|
|
showline=True,
|
|
side="right",
|
|
tickfont={"color": "#DC3912"},
|
|
tickmode="auto",
|
|
ticks="",
|
|
titlefont={"color": "#DC3912"},
|
|
type="linear",
|
|
zeroline=False,
|
|
),
|
|
|
|
yaxis2=dict(
|
|
anchor="x",
|
|
autorange=True,
|
|
domain=[0.11, 0.2],
|
|
linecolor="#3366CC",
|
|
mirror=True,
|
|
showline=True,
|
|
side="right",
|
|
tickfont={"color": "#3366CC"},
|
|
tickmode="auto",
|
|
ticks="",
|
|
titlefont={"color": "#3366CC"},
|
|
type="linear",
|
|
zeroline=False,
|
|
),
|
|
|
|
yaxis3=dict(
|
|
anchor="x",
|
|
autorange=True,
|
|
domain=[0.21, 0.35],
|
|
linecolor="#222A2A",
|
|
mirror=True,
|
|
showline=True,
|
|
side="right",
|
|
tickfont={"color": "#222A2A"},
|
|
tickmode="auto",
|
|
ticks="",
|
|
titlefont={"color": "#222A2A"},
|
|
type="linear",
|
|
zeroline=True,
|
|
),
|
|
|
|
yaxis4=dict(
|
|
anchor="x",
|
|
autorange=True,
|
|
domain=[0.36, 0.45],
|
|
linecolor="#795548",
|
|
mirror=True,
|
|
showline=True,
|
|
side="right",
|
|
tickfont={"color": "#795548"},
|
|
tickmode="auto",
|
|
ticks="",
|
|
titlefont={"color": "#795548"},
|
|
type="linear",
|
|
zeroline=False,
|
|
|
|
),
|
|
|
|
|
|
yaxis5=dict(
|
|
anchor="x",
|
|
autorange=True,
|
|
domain=[0.46, 0.55],
|
|
linecolor="#673ab7",
|
|
mirror=True,
|
|
showline=True,
|
|
side="right",
|
|
tickfont={"color": "#673ab7"},
|
|
tickmode="auto",
|
|
ticks="",
|
|
titlefont={"color": "#673ab7"},
|
|
type="linear",
|
|
zeroline=False,
|
|
|
|
),
|
|
|
|
|
|
|
|
yaxis6=dict(
|
|
anchor="x",
|
|
autorange=True,
|
|
domain=[0.56, 1],
|
|
linecolor="#E91E63",
|
|
mirror=True,
|
|
showline=True,
|
|
side="right",
|
|
tickfont={"color": "#E91E63"},
|
|
tickmode="auto",
|
|
ticks="",
|
|
titlefont={"color": "#E91E63"},
|
|
type="linear",
|
|
zeroline=False,
|
|
|
|
))
|
|
|
|
#----------------------------------------------------------------------------------
|
|
# final settings
|
|
#----------------------------------------------------------------------------------
|
|
|
|
fig.update_traces(decreasing_line_width=2,increasing_line_width=2,whiskerwidth=0.5, selector=dict(type='candlestick'))
|
|
|
|
if freq=='B':
|
|
fig.update_xaxes(rangebreaks=[
|
|
dict(bounds=['sat', 'mon'])])
|
|
|
|
if freq[-1]=='T':
|
|
fig.update_xaxes(rangebreaks=[dict(bounds=[14, 9], pattern="hour"),
|
|
dict(bounds=['sat', 'mon'])])
|
|
|
|
|
|
|
|
fig.update_layout(
|
|
title = f'STOCK ID : {stock_id}, Data frequency:{freq}',
|
|
dragmode="zoom",
|
|
hovermode="x",
|
|
legend=dict(orientation="h",
|
|
yanchor="top",
|
|
xanchor="center",
|
|
y=-0.3,
|
|
x=0.5,
|
|
traceorder="reversed"),
|
|
height=1000,
|
|
width=1000,
|
|
template="plotly_white",
|
|
margin=dict(
|
|
l = 70,
|
|
r = 70,
|
|
t=100,
|
|
b=50
|
|
),
|
|
)
|
|
|
|
fig.show() |