每天更新的台股歷史資料庫，計算技術指標，回測然後推薦股票。
https://voidful.github.io/tw_stocker/stock_report.html
import pandas as pd
url="https://raw.githubusercontent.com/voidful/tw_stocker/main/data/2330.csv"
pd.read_csv(url)Yahoo finance，每隔5分鐘的六十天內資料，會用github action持續更新。
pip install fta
import pandas as pd
import fta
url = "https://raw.githubusercontent.com/voidful/tw_stocker/main/data/2330.csv"
df = pd.read_csv(url, index_col='Datetime')
ta = fta.TA_Features()
df_full = ta.get_all_indicators(df)
print(df_full)- git clone this project
- 參考
strategy/dynamic_delay作為我們交易的策略
import pandas as pd
import fta
from strategy.dynamic_delay import trade
url = "https://raw.githubusercontent.com/voidful/tw_stocker/main/data/2330.csv"
df = pd.read_csv(url, index_col='Datetime')
ta = fta.TA_Features()
df_full = ta.get_all_indicators(df)
PARAMETER = {
"delay": 15,
"initial_money": 10000,
"max_buy": 10,
"max_sell": 10,
}
states_buy, states_sell, states_entry, states_exit, total_gains, invest = trade(df_full, **PARAMETER)
from matplotlib import pyplot as plt
import pandas as pd
import fta
from strategy.dynamic_delay import trade
url = "https://raw.githubusercontent.com/voidful/tw_stocker/main/data/2330.csv"
df = pd.read_csv(url, index_col='Datetime')
ta = fta.TA_Features()
df_full = ta.get_all_indicators(df)
PARAMETER = {
"delay": 15,
"initial_money": 10000,
"max_buy": 10,
"max_sell": 10,
}
states_buy, states_sell, states_entry, states_exit, total_gains, invest = trade(df_full, **PARAMETER)
close = df_full['close']
fig = plt.figure(figsize = (15,5))
plt.plot(close, color='r', lw=2.)
plt.plot(close, '^', markersize=10, color='m', label = 'buying signal', markevery = states_buy)
plt.plot(close, 'v', markersize=10, color='k', label = 'selling signal', markevery = states_sell)
plt.legend()
plt.show()
import vectorbt as vbt
import pandas as pd
import numpy as np
import fta
from strategy.dynamic_delay import trade
url = "https://raw.githubusercontent.com/voidful/tw_stocker/main/data/2330.csv"
df = pd.read_csv(url, index_col='Datetime')
ta = fta.TA_Features()
df_full = ta.get_all_indicators(df)
PARAMETER = {
"delay": 15,
"initial_money": 10000,
"max_buy": 10,
"max_sell": 10,
}
states_buy, states_sell, states_entry, states_exit, total_gains, invest = trade(df_full, **PARAMETER)
fees = 0 # 假設交易費用為 0
portfolio_kwargs = dict(size=np.inf, fees=float(fees), freq='5m')
portfolio = vbt.Portfolio.from_signals(df_full['close'], states_entry, states_exit, **portfolio_kwargs)
print(portfolio.stats())
portfolio.plot().show()
