kevinhug commited on
Commit
da9cbbc
·
1 Parent(s): db20647
Files changed (2) hide show
  1. app.py +24 -4
  2. requirements.txt +4 -3
app.py CHANGED
@@ -13,6 +13,7 @@ TIME SERIES ANALYTICS
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  import yfinance as yf
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  import pandas as pd
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  from sklearn.preprocessing import StandardScaler
 
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  def trend(t):
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  '''
@@ -31,6 +32,23 @@ def trend(t):
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  d=df.tail(1).stack(level=-1).droplevel(0, axis=0)
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  '''
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  d=pd.read_pickle("data.pkl")
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  return gr.ScatterPlot(d, x="Close_MA", y="Volume_MA",color='ticker')
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  '''
@@ -224,13 +242,15 @@ Use Case:
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  Maximizing Trading Efficiency: Personalize Your Asset Allocation for Optimal Growth
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  =========
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- The industry life cycle is a useful tool for traders to identify growth and decline industries. It describes the evolution of an industry based on its stages of growth and decline 1. There are four phases of the industry life cycle: introduction, growth, maturity, and decline 2.
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- A growth industry is a sector of an economy that experiences a higher-than-average growth rate compared to other sectors. Growth industries are often new or pioneer industries that did not exist in the past. Their growth is a result of demand for new products or services offered by companies in the field 3. Identifying growth industries can help traders to speed up trading by investing in companies that are likely to experience rapid growth in the future.
 
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- On the other hand, a decline industry is a sector of an economy that is experiencing a lower-than-average growth rate compared to other sectors. Identifying decline industries can help traders to avoid investing in companies that are likely to experience a decline in the future 2. This can help traders to minimize losses and maximize profits.
 
 
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- By identifying growth and decline industries, traders can make informed investment decisions and speed up trading by investing in companies that are likely to experience growth in the future and avoiding companies that are likely to experience a decline in the future.
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  """)
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  demo.launch()
 
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  import yfinance as yf
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  import pandas as pd
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  from sklearn.preprocessing import StandardScaler
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+ import plotly.express as px
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  def trend(t):
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  '''
 
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  d=df.tail(1).stack(level=-1).droplevel(0, axis=0)
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  '''
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  d=pd.read_pickle("data.pkl")
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+ '''
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+ fig = px.line(df, x="day", y=countries)
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+ fig.update_layout(
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+ title="Outbreak in " + month,
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+ xaxis_title="Cases",
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+ yaxis_title="Days Since Day 0",
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+ )
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+ return fig
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+ '''
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+ fig=px.scatter(d, x="Close_MA", y="Volume_MA",color='ticker')
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+ fig.update_layout(
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+ title="Top Right is the Growth Industry",
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+ xaxis_title="Trend in Price",
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+ yaxis_title="Trend in Volume",
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+ )
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+ return fig
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+ return
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  return gr.ScatterPlot(d, x="Close_MA", y="Volume_MA",color='ticker')
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  '''
 
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  Maximizing Trading Efficiency: Personalize Your Asset Allocation for Optimal Growth
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  =========
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+ The industry life cycle is a useful tool for traders to identify growth and decline industries. It describes the evolution of an industry based on its stages of growth and decline
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+ #### There are four phases of the industry life cycle: introduction, growth, maturity, and decline
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+ - By identifying growth and decline industries, traders can make informed investment decisions and speed up trading by investing in companies that are likely to experience growth in the future and avoiding companies that are likely to experience a decline in the future.
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+ #### Personalize objective function and cost function
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+ - cost function can prevent selecting decline industry
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+ - objective function can identify potential industry
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  """)
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  demo.launch()
requirements.txt CHANGED
@@ -1,6 +1,7 @@
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  chromadb
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  fastai
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- pyarrow
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  pandas==2.1.3
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- yfinance==0.2.31
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- scikit-learn
 
 
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  chromadb
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  fastai
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+ #pyarrow
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  pandas==2.1.3
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+ #yfinance==0.2.31
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+ #scikit-learn
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+ plotly