practice SVM.dft1.TAIEX.POC.0628 from google.colab import drive drive.mount('/content/drive') !pip install yfinance scikit-learn matplotlib statsmodels ta import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from sklearn.svm import SVR from sklearn.decomposition import PCA
practice Random_forest(dft.4)_TAIEX_0628 加入 beta,beta_120 beta_120的離散大 from google.colab import drive drive.mount('/content/drive') !pip install yfinance scikit-learn matplotlib statsmodels import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from sklearn.ensemble import RandomForestRegressor
practice Random_Forest(dft.3)_TAIEX_POC_0628 * n_iter=10：這意味著隨機搜索將嘗試 10 種不同的參數組合。您可以根據時間和資源的限制來調整這個數字。 * cv=3：使用 3 折交叉驗證來評估每個參數組合，這可以在保持模型性能評估的同時減少計算時間。 from google.colab import drive drive.mount('/content/drive') !pip install yfinance scikit-learn matplotlib statsmodels import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing
practice 如何獲取 TAIEX 成分股代碼 !pip install pandas requests gspread oauth2client import pandas as pd import requests import gspread from google.colab import auth from oauth2client.client import GoogleCredentials from google.colab import drive # 掛載 Google Drive drive.mount('/content/drive') # 獲取 TAIEX 成分股代碼 url = "https://isin.twse.com.tw/isin/C_
practice Random_Forest(dft.2)_TAIEX_POC_0628 Fitting 2 folds for each of 20 candidates, totalling 40 fits Training MSE: 5453.5616 Testing MSE: 11776567.8450 Training MAE: 53.8584 Testing MAE: 3206.9294 OLS Regression 移除 BB_Upper,BB_Lower OLS Regression Results ============================================================================== Dep. Variable: ^TWII R-squared: 0.803 Model: OLS
practice Random_Forest(dft.1)_TAIEX_POC_0628 OLS Regression import yfinance as yf import pandas as pd import numpy as np import statsmodels.api as sm # 定義股票代碼和大盤指數 tickers = ["2330.TW", "2454.TW", "2317.TW", "2412.TW", "1303.TW", "2882.TW", "3008.TW", "
practice Random_Forest_(dft.0).TAIEX.POC.0628 from google.colab import drive drive.mount('/content/drive') !pip install yfinance scikit-learn matplotlib import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from sklearn.ensemble import RandomForestRegressor import matplotlib.dates as mdates # 定義股票代碼和大盤指數 tickers
practice ARIMA.TAIEX.POC.0628 Simple ARIMA Model import pandas as pd import numpy as np import matplotlib.pyplot as plt import yfinance as yf from sklearn.preprocessing import MinMaxScaler from statsmodels.tsa.arima.model import ARIMA import datetime from google.colab import drive # 授權 Google Drive drive.mount('/content/drive') # 下載資料的函數 def
practice Linear Regress.TAIEX_POC.0628 1'st: 2024-06-28 最簡單的模型 import pandas as pd import numpy as np import matplotlib.pyplot as plt import yfinance as yf from sklearn.linear_model import LinearRegression from sklearn.preprocessing import MinMaxScaler from google.colab import drive import datetime # 授權 Google Drive drive.mount('/content/drive') # 定義下載資料的函數
practice Random_Forest_TAIEX_POC_0628 dataset: MSCI top 30 from google.colab import drive drive.mount('/content/drive') !pip install yfinance scikit-learn matplotlib import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from sklearn.ensemble import RandomForestRegressor import matplotlib.dates
practice LSTM.TAIEX.POC.0628. dataset: MSCI top 30 company from google.colab import drive drive.mount('/content/drive') !pip install yfinance scikit-learn matplotlib tensorflow import yfinance as yf import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler from tensorflow.keras.models import Sequential
practice LSTM_TAIEX_0628 training 能力不好,要再調 特徵變數（Features） 特徵變數是用來預測目標變數的獨立變數。在這個例子中，特徵變數包括以下幾個部分： 1. Open: 當日的開盤價。 2. High: 當日的最高價。 3. Low: 當日的最低價。 4. Close: 當日的收盤價。 5. Adj Close: 經過調整的收盤價，考慮了股息和拆股等因素。 6. Volume: 當日的交易量。 7. Daily Change: 當日收盤價的日變動率，計算公式為 (當日收盤價 - 前一日收盤價) / 前一日收盤價。 8. Beta_60: 過去60天的Beta值，用於衡量股票相對於大盤的波動性。 9. Beta_120: 過去120天的Beta值，用於衡量股票相對於大盤的波動性。 目標變數（Target） 目標變數是我們想要預測的變數。
Economic Exploring the Kuznets Curve of Alzheimer's Disease (Draft.0624) update: 2024-06-24 ABSTRACT This study explores the relationship between economic development and Alzheimer's disease prevalence, conceptualized as the 'Kuznets Curve of Alzheimer's Disease'. Using data from 1990 to 2018 across 183 countries, we analyze the correlation between GDP per capita and Alzheimer's
practice LSTM.2542.POC.0619 !pip install yfinance import yfinance as yf # 下載0050過去5年的股價數據 ticker = '0050.TW' stock_data = yf.download(ticker, period='5y') stock_data.reset_index(inplace=True) stock_data.to_csv('0050_stock_data.csv', index=False) import pandas as pd import numpy as np import matplotlib.
Economic xtsemi.log_az_N. + ln_az_N 0627 update: 2024-06-19 semipar xi:semipar az100k pop_65y_pct primaryedu_year hyperten_100k depress100k nonhdl_mgd i.year,nonpar(gdppp2017) ci i.year _Iyear_1990-2018 (naturally coded; _Iyear_1990 omitted) xtseimpar log_az N generate logaz=log(az100k) generate lnaz=ln(az100k) generate log_gdp=log(gdppp2017) generate log_pop65=
Economic semipar az updated: 2024-06-19 xi:semipar az100k pop_65y_pct primaryedu_year hyperten_100k depress100k nonhdl_mgdl pvr_bmi30 i.year,nonpar(gdppp2017) ci
Essay 阿娘 0:00/190.9441× 阿娘 戰火紛飛的童年 寂靜港灣一樣閃耀的星光點點 妳的愛像是湛藍料羅岸 我們感受無盡的溫暖 阿娘 妳是我心中的光 無論多遠 妳的愛在身旁 妳用雙手撐起了希望 讓我飛翔，勇敢無懼地闖 清晨五點妳悄然起床 為我準備每一頓早餐 那荷包蛋和炸排骨香 是我們記憶中最美麗的一章 阿娘 妳是我們心中的光 無論多遠 妳的愛在身旁 妳用雙手撐起了希望 讓我飛翔 勇敢無懼地闖 妳的愛像陽光灑滿心田 溫暖著我每一天 在挫折中跌倒 妳的笑容是我前行的力量 阿娘 妳是我心中的光 無論多遠 妳的愛在身旁 妳用雙手撐起了希望 讓我飛翔 勇敢無懼地闖 阿娘 妳是永恆的光芒 在我的生命中閃耀 妳的愛將我照亮 我知道妳一直在我身旁
practice 2303.LSTM.1'st.practice !pip install yfinance import yfinance as yf # 下載2303過去5年的股價數據 ticker = '2303.TW' stock_data = yf.download(ticker, period='5y') stock_data.reset_index(inplace=True) stock_data.to_csv('2303_stock_data.csv', index=False) # 預處理 import pandas as pd import numpy as np import
practice 0050.XGB.2nd.practice 2024-06-19 # 安裝必要的庫 XGB !pip install yfinance xgboost import pandas as pd import numpy as np import matplotlib.pyplot as plt import yfinance as yf from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error import xgboost as xgb import math # 下載0050過去5年的股價數據 ticker = '0050.TW' stock_data
practice 0050.XGB.180D. 1'st.practice 2024-06-19 !pip install yfinance import yfinance as yf # 下載0050過去5年的股價數據 ticker = '0050.TW' stock_data = yf.download(ticker, period='5y') stock_data.reset_index(inplace=True) stock_data.to_csv('0050_stock_data.csv', index=False) import pandas as pd import numpy as np import
practice 2303.transformer180D 2024-06-19 import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.preprocessing import MinMaxScaler import math from sklearn.metrics import mean_squared_error import tensorflow as tf from tensorflow.keras.models import Model from tensorflow.keras.layers import Input, Dense, Flatten, LayerNormalization, MultiHeadAttention, Dropout # Load
practice 2303_XGB_Predict180D update by 6/19 # 安裝必要的庫 !pip install yfinance xgboost import pandas as pd import numpy as np import matplotlib.pyplot as plt import yfinance as yf from sklearn.preprocessing import MinMaxScaler from sklearn.metrics import mean_squared_error import xgboost as xgb import math # 下載2303過去5年的股價數據 ticker = '2303.TW'
2303 LSTM.Predict,180D 6/19 update !pip install yfinance import yfinance as yf # 下載2303過去5年的股價數據 ticker = '2303.TW' stock_data = yf.download(ticker, period='5y') stock_data.reset_index(inplace=True) stock_data.to_csv('2303_stock_data.csv', index=False) # 預處理 import pandas as pd import numpy
