Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/voidful/tw_stocker
keep tracking and store taiwan stock information - 每天更新台股歷史資料庫
https://github.com/voidful/tw_stocker
stock taiwan taiwan-stock-market
Last synced: 3 days ago
JSON representation
keep tracking and store taiwan stock information - 每天更新台股歷史資料庫
- Host: GitHub
- URL: https://github.com/voidful/tw_stocker
- Owner: voidful
- Created: 2024-04-08T18:02:08.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-02-03T16:19:42.000Z (5 days ago)
- Last Synced: 2025-02-04T04:19:04.002Z (4 days ago)
- Topics: stock, taiwan, taiwan-stock-market
- Language: Python
- Homepage: https://github.com/voidful/tw_stocker/tree/main/data
- Size: 3.97 GB
- Stars: 107
- Watchers: 4
- Forks: 35
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# TW Stocker
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
## 使用方式，以2330為例，可以換成自己需要的股票
```python
import pandas as pd
url="https://raw.githubusercontent.com/voidful/tw_stocker/main/data/2330.csv"
pd.read_csv(url)
```
## 資料來源
Yahoo finance，每隔5分鐘的六十天內資料，會用github action持續更新。
## 抽取技術指標
1. `pip install fta`
2.
```python
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)
```
## 模擬交易
1. git clone this project
2. 參考`strategy/dynamic_delay`作為我們交易的策略
```python
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)
```
#### 結果
### 交易圖表
```python
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()
```
#### 結果
### 回測
```python
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()
```
#### 結果
