test

.vscode/settings.json

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+{
+    "python.pythonPath": "C:\\Users\\Seb\\AppData\\Local\\Programs\\Python\\Python39\\python.exe",
+    "jupyter.jupyterServerType": "remote"
+}

GetDaily.py

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+#Get stock symbols in a file
+#Get daily quote at end of day
+#Send email, see Jupyter thing on other laptop for sending email
+
+#Quandl no more data after 2018 for some reason
+
+import quandl
+import numpy as np 
+import yfinance as yf
+import plotly.graph_objects as go
+
+#Quandl test
+quandl.ApiConfig.api_key = "sEj_5XNt1kyxi27p5nre"
+amazon = quandl.get("WIKI/AMZN")
+print(amazon.head())
+print(amazon.tail(10))
+
+#Yfinance tests
+df = yf.download("TSLA", start="2018-11-01", end="2020-10-18", interval="1d")
+fig = go.Figure(
+    data=go.Ohlc(
+        x=df.index,
+        open=df["Open"],
+        high=df["High"],
+        low=df["Low"],
+        close=df["Close"],
+    )
+)
+fig.show()

test.py

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+#DataFlair - Make necessary imports
+import quandl
+import numpy as np 
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.model_selection import train_test_split
+
+
+#DataFlair - Get Amazon stock data
+amazon = quandl.get("WIKI/AMZN")
+print(amazon.head())
+
+
+#DataFlair - Get only the data for the Adjusted Close column
+amazon = amazon[['Adj. Close']]
+print(amazon.head())
+
+
+#DataFlair - Predict for 30 days; Predicted has the data of Adj. Close shifted up by 30 rows
+forecast_len=30
+amazon['Predicted'] = amazon[['Adj. Close']].shift(-forecast_len)
+print(amazon.tail())
+
+#DataFlair - Drop the Predicted column, turn it into a NumPy array to create dataset
+x=np.array(amazon.drop(['Predicted'],1))
+#DataFlair - Remove last 30 rows
+x=x[:-forecast_len]
+print(x)
+
+
+#DataFlair - Create dependent dataset for predicted values, remove the last 30 rows
+y=np.array(amazon['Predicted'])
+y=y[:-forecast_len]
+print(y)
+
+
+#DataFlair - Split datasets into training and test sets (80% and 20%)
+x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)
+
+
+#DataFlair - Create SVR model and train it
+svr_rbf=SVR(kernel='rbf',C=1e3,gamma=0.1) 
+svr_rbf.fit(x_train,y_train)
+
+
+#DataFlair - Get score
+svr_rbf_confidence=svr_rbf.score(x_test,y_test)
+print(f"SVR Confidence: {round(svr_rbf_confidence*100,2)}%")
+
+
+#DataFlair - Create Linear Regression model and train it
+lr=LinearRegression()
+lr.fit(x_train,y_train)
+
+
+#DataFlair - Get score for Linear Regression
+lr_confidence=lr.score(x_test,y_test)
+print(f"Linear Regression Confidence: {round(lr_confidence*100,2)}%")
+