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import matplotlib.pyplot as plt | |
from datetime import timedelta | |
import seaborn as sns | |
from celluloid import Camera | |
camera = Camera(plt.figure(figsize=(17, 9))) | |
sns.set() | |
df1_train["type"] = "lightcoral" | |
df1_test["type"] = "mediumblue" | |
new = pd.concat([df1_train, df1_test]) |
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import matplotlib.pyplot as plt | |
from datetime import timedelta | |
import seaborn as sns | |
from celluloid import Camera | |
camera = Camera(plt.figure(figsize=(17, 9))) | |
sns.set() | |
df1_train["type"] = "lightcoral" | |
df1_test["type"] = "mediumblue" | |
new = pd.concat([df1_train, df1_test]) |
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import matplotlib.pyplot as plt | |
import seaborn as sns | |
from celluloid import Camera | |
from datetime import timedelta | |
camera = Camera(plt.figure(figsize=(17, 9))) | |
sns.set() | |
df1_train["type"] = "lightcoral" | |
df1_test["type"] = "mediumblue" | |
new = pd.concat([df1_train, df1_test]) |
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import yfinance as yf | |
import pandas as pd | |
import ta | |
data = yf.download("AMZN", start="2020-01-01", end="2021-01-29", interval='1d', | |
group_by="ticker") | |
df = pd.DataFrame(data) | |
df1 = ta.add_all_ta_features( | |
df, open="Open", high="High", low="Low", close="Close", volume="Volume") |
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import yfinance as yf | |
import pandas as pd | |
import ta | |
data = yf.download("AMC", start="2020-01-01", end="2021-01-29", interval='1d', | |
group_by="ticker") | |
df = pd.DataFrame(data) | |
df1 = ta.add_all_ta_features( | |
df, open="Open", high="High", low="Low", close="Close", volume="Volume") |
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import yfinance as yf | |
import pandas as pd | |
import ta | |
data = yf.download("KODK", start="2020-01-01", end="2021-01-29", interval='1d', | |
group_by="ticker") | |
df = pd.DataFrame(data) | |
df1 = ta.add_all_ta_features( | |
df, open="Open", high="High", low="Low", close="Close", volume="Volume") |
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import yfinance as yf | |
import pandas as pd | |
import ta | |
data = yf.download("NOK", start="2020-01-01", end="2021-01-29", interval='1d', | |
group_by="ticker") | |
df = pd.DataFrame(data) | |
df1 = ta.add_all_ta_features( | |
df, open="Open", high="High", low="Low", close="Close", volume="Volume") |
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import yfinance as yf | |
import pandas as pd | |
import ta | |
data = yf.download("HTZGQ", start="2018-01-01", end="2021-01-29", interval='1d', | |
group_by="ticker") | |
df = pd.DataFrame(data) | |
df1 = ta.add_all_ta_features( | |
df, open="Open", high="High", low="Low", close="Close", volume="Volume") |
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import yfinance as yf | |
import pandas as pd | |
import ta | |
data = yf.download("GME", start="2018-01-01", end="2021-01-29", interval='1d', | |
group_by="ticker") | |
df = pd.DataFrame(data) | |
df1 = ta.add_all_ta_features( | |
df, open="Open", high="High", low="Low", close="Close", volume="Volume") |
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import yfinance as yf | |
import pandas as pd | |
import ta | |
data = yf.download("GME", start="2021-01-01", end="2021-01-29", interval='90m', | |
group_by="ticker") | |
df = pd.DataFrame(data) | |
df1 = ta.add_all_ta_features( | |
df, open="Open", high="High", low="Low", close="Close", volume="Volume") |
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