黄金矿工

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#conda envs:vectorbt_env
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import warnings
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import vectorbt as vbt
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import numpy as np
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import pandas as pd
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from datetime import datetime, timedelta
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import pytz
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from dateutil.parser import parse
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import ipywidgets as widgets
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from copy import deepcopy
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from tqdm import tqdm
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import imageio
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from IPython import display
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import plotly.graph_objects as go
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import itertools
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import dateparser
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import gc
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import math
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from tools import dbtools
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warnings.filterwarnings("ignore")
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pd.set_option('display.max_rows',500)
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pd.set_option('display.max_columns',500)
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pd.set_option('display.width',1000)

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# fig.show_svg()
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upper_window = 20 # 上边界滚动计算周期
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lower_window = 10 # 下边界滚动计算周期
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upper_window_comp_skip = 5  # 突破比较时需要和前n日的比较
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lower_window_comp_skip = 3  # 突破比较时需要和前n日的比较
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# Pre-calculate running windows on data with time buffer
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upper_band = vbt.talib('MAX').run(ohlcv_wbuf['High'], timeperiod=upper_window)
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# print(upper_band.real.head(30))
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lower_band = vbt.talib('MIN').run(ohlcv_wbuf['Low'], timeperiod=lower_window)
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print(upper_band.real.shape)
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print(lower_band.real.shape)
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# Remove time buffer
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upper_band = upper_band[wobuf_mask]
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lower_band = lower_band[wobuf_mask]
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# there should be no nans after removing time buffer
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assert(~upper_band.real.isnull().any())
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assert(~lower_band.real.isnull().any())
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print(upper_band.real.shape)
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print(lower_band.real.shape)
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fig = ohlcv['Close'].vbt.plot(trace_kwargs=dict(name='Price'))
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fig = upper_band.real.vbt.plot(trace_kwargs=dict(name='Upper Band'), fig=fig)
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fig = lower_band.real.vbt.plot(trace_kwargs=dict(name='Lower Band'), fig=fig)
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fig.show_svg()

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(1409,)
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(1409,)
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(485,)
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(485,)

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# def crossed_above(series1, series2):
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#     """Returns a boolean Series indicating where series1 crosses above series2."""
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#     return (series1 > series2) & (series1.shift(1) <= series2.shift(1))
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# def crossed_below(series1, series2):
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#     """Returns a boolean Series indicating where series1 crosses below series2."""
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#     return (series1 < series2) & (series1.shift(1) >= series2.shift(1))
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# dc_entries = crossed_above(ohlcv['Close'],upper_band.real.shift(5).fillna(method='ffill'))# 后移5/3天，当天视角看就是close比对3天前的upper/lower
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# dc_exits =  crossed_below(ohlcv['Close'],lower_band.real.shift(3).fillna(method='ffill'))
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# 信号计算
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upper_band_nd_ago = upper_band.real.shift(upper_window_comp_skip) #n日前max价格
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lower_band_nd_ago = lower_band.real.shift(lower_window_comp_skip) #
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# Long when MACD is above zero AND signal
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# 优化：ohlcv_wbuf["Close"]->ohlcv_wbuf["High"]
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# 原理：最高价不断走高的是好行情，最高价包含情绪因素，比收盘价更具代表性
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dc_entries = ohlcv["Close"][upper_band_nd_ago.index].vbt.crossed_above(upper_band_nd_ago)
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dc_exits= ohlcv["Close"][lower_band_nd_ago.index].vbt.crossed_below(lower_band_nd_ago)
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# 行情-指标-信号可视化
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fig = ohlcv['Close'].vbt.plot(trace_kwargs=dict(name='Price'))
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fig = upper_band.real.vbt.plot(trace_kwargs=dict(name='Fast MA'), fig=fig)
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fig = lower_band.real.vbt.plot(trace_kwargs=dict(name='Slow MA'), fig=fig)
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fig = dc_entries.vbt.signals.plot_as_entry_markers(ohlcv['Close'], fig=fig)
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fig = dc_exits.vbt.signals.plot_as_exit_markers(ohlcv['Close'], fig=fig)
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fig.show_svg()
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# (单独)信号可视化
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fig = dc_entries.vbt.signals.plot(trace_kwargs=dict(name='Entries'))
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dc_exits.vbt.signals.plot(trace_kwargs=dict(name='Exits'), fig=fig).show_svg()
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# 信号的统计信息
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dc_entries.vbt.signals.stats(settings=dict(other=dc_exits))

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Start                       2021-01-04 00:00:00+00:00
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End                         2022-12-30 00:00:00+00:00
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Period                                            485
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Total                                              19
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Rate [%]                                     3.917526
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Total Overlapping                                   0
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Overlapping Rate [%]                              0.0
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First Index                 2021-01-18 00:00:00+00:00
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Last Index                  2022-08-29 00:00:00+00:00
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Norm Avg Index [-1, 1]                       -0.19552
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Distance -> Other: Min                            3.0
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Distance -> Other: Max                           67.0
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Distance -> Other: Mean                     25.315789
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Distance -> Other: Std                       17.34952
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Total Partitions                                   19
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Partition Rate [%]                              100.0
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Partition Length: Min                             1.0
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Partition Length: Max                             1.0
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Partition Length: Mean                            1.0
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Partition Length: Std                             0.0
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Partition Distance: Min                           2.0
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Partition Distance: Max                          61.0
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Partition Distance: Mean                    21.722222
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Partition Distance: Std                     18.549792
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dtype: object