Upload companies.py

apps/companies.py

@@ -1,3 +1,322 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a3b9f15051f8a9b2d45d029fc5b4dc55ebe2801e3f35ee377096b8fc641fa4e7
-size 13398
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Apr 26 17:38:54 2022
+
+@author: bullm
+"""
+
+import streamlit as st
+from modules import tables
+import pandas as pd
+from streamlit_echarts import st_echarts
+from Data.credentials import credentials_s3 as creds3
+import boto3
+import io
+import pybase64 as base64
+import matplotlib.pyplot as plt
+
+
+@st.experimental_memo
+def get_asset_field(id_quant, start, field='IQ_CLOSEPRICE_ADJ', expand=True,
+                    rename=['asset']):
+    asset_obj = tables.EquityMaster(asset=id_quant, field=field)
+    asset_df = asset_obj.query(rename=rename, start=start, expand=expand)
+    return pd.DataFrame(asset_df)
+
+
+@st.experimental_memo
+def get_macro_field(country, start, instrument="INDEX", expand=True,
+                    rename=['country']):
+    asset_obj = tables.MacroMaster(country=country, instrument=instrument)
+    asset_df = asset_obj.query(rename=rename, start=start, expand=expand)
+    return pd.DataFrame(asset_df)
+
+
+def plot_returns(id_quant, country, start):
+    asset_df = get_asset_field(id_quant, start)
+    index_df = get_macro_field(country, start)
+    asset_df = asset_df.merge(index_df, how='left',
+                              left_index=True,
+                              right_index=True)
+    x = asset_df.index
+    y2 = asset_df[id_quant]/asset_df.iloc[0][id_quant] - 1
+    y1= (1 + asset_df[country]).cumprod() - 1
+    plt.figure(figsize=(10, 5))
+    plt.rcParams['axes.facecolor'] = '#EAEAEA'
+    plt.rcParams['figure.facecolor'] = '#EAEAEA'
+    plt.fill_between(x, y1, y2, where=y2 >y1, facecolor='green', alpha=0.5)
+    plt.fill_between(x, y1, y2, where=y2 <=y1, facecolor='red', alpha=0.5)
+    plt.xticks(rotation=60)
+    plt.title('Asset vs Benchmark')
+    st.pyplot(plt, height='300')
+
+
+def get_ebitda(id_quant):
+    ebitda_df = get_asset_field(id_quant, '2021-01-01', field='IQ_EBITDA', expand=True,
+                    rename=['asset'])
+    ebitda_actual = round(ebitda_df.iloc[-1][id_quant], 2)
+    ebitda_anterior =  round(ebitda_df.iloc[-2][id_quant], 2)
+    delta = round(ebitda_actual - ebitda_anterior,2)
+    st.metric("Ebitda " + ebitda_df.index[-1].strftime("%Y-%m-%d"), ebitda_actual, delta)
+
+
+
+
+
+
+
+
+
+
+@st.experimental_memo
+def get_asset_field(id_quant, field, start, expand=False, rename=['asset', 'field']):
+    asset_obj = tables.EquityMaster(asset=id_quant, field=field)
+    asset_df = asset_obj.query(rename=rename, start=start, expand=expand)
+    return pd.DataFrame(asset_df)
+
+@st.experimental_memo
+def get_macro_field(country, instrument, start, expand=True, rename=['country']):
+    asset_obj = tables.MacroMaster(country=country, instrument=instrument)
+    asset_df = asset_obj.query(rename=rename, start=start, expand=expand)
+    return pd.DataFrame(asset_df)
+
+def get_dict_companies():
+    company_base_df = pd.read_excel("Data/Company_Base_Definitivo.xlsx",
+                                    sheet_name='Compilado')
+    company_id_dict = dict(zip(company_base_df["Ticker"], company_base_df["ID_Quant"]))
+    return company_id_dict
+    # asset = data_daily[field][id_quant]
+
+def read_itub():
+    itub_df = pd.read_csv('C:/Users/bullm/Desktop/ITUB.csv')
+    itub_df.index = pd.to_datetime(itub_df["Date"])
+    itub_cs_s = itub_df["Adj Close"]
+    st.line_chart(itub_cs_s)
+
+
+def company_info():
+    st.set_page_config(layout="wide", page_title="Portal LVAM",
+                       page_icon="img/icono.png")
+    st.sidebar.write("Companies")
+    
+    company_base_df = pd.read_excel("Data/Company_Base_Definitivo.xlsx",
+                                    sheet_name='Compilado')
+    col1, col2 = st.columns((1, 1.681))
+    companies_id_dict = get_dict_companies()
+    tickers = col2.multiselect("Seleccionasr Empresa",
+                               company_base_df["Ticker"],
+                               ["ITUB4"])
+    country = col2.multiselect("Seleccionasr Empresa",
+                               company_base_df["Portfolio_Country"].unique(),
+                               ["Brazil"])
+    id_quants= [str(companies_id_dict[ticker]) for ticker in tickers]
+    fields_ls= ["IQ_CLOSEPRICE_ADJ", "IQ_MARKETCAP"]
+    field = col1.selectbox("Selecione un campo", fields_ls)
+    start = '2020-01-01'
+    df = get_asset_field(id_quants, field, start, rename=['asset'])
+    df = df.ffill(axis=0)
+    tickers = list(tickers)
+    company_id_dict = dict(zip(company_base_df["Ticker"], company_base_df["ID_Quant"]))
+    id_company_dict = dict(zip(company_base_df["ID_Quant"], company_base_df["Ticker"]))
+    df.columns = [id_company_dict[int(col)] for col in df.columns]
+    st.title('Cierre Ajustado Mongo Quant')
+    col1, col2, col3 = st.columns(3)
+    mm2 = col2.checkbox("Indice Pais")
+    mm3 = col3.checkbox("Indice Sector")
+    if len(tickers) == 1:
+        mm = col1.checkbox("Medias moviles")
+        rollings = [20,60,240]
+        dicc_mm = {
+            tickers[0] + f' {x}':df[tickers[0]].rolling(x).mean() for x in rollings
+            }
+        df2 =pd.concat(dicc_mm.values(), keys=dicc_mm.keys(), axis=1)
+        df = pd.concat([df, df2], axis=1)
+        if mm2:
+            mc_df = (1+get_macro_field(country, "INDEX", start)).cumprod()
+            df = pd.concat([df, mc_df], axis=1).ffill(axis=0)
+            df = df.iloc[len(df) - 252: ]
+            
+        else:
+            df = df.iloc[len(df) - 252: ]
+        if not mm and not mm2:
+            st.write(df)
+            st.line_chart(df[df.columns[0]])
+        elif not mm and mm2:
+            df = df[[df.columns[0],df.columns[-1]]]/df.iloc[0][[df.columns[0],df.columns[-1]]]
+            st.write(df)
+            st.line_chart(df)
+        else:
+            st.write(df)
+            st.line_chart(df)
+    if len(tickers) > 1:
+        if mm2:
+            mc_df = (1+get_macro_field(country, "INDEX", start)).cumprod()
+            df = pd.concat([df, mc_df], axis=1).ffill(axis=0)
+            if mm3:
+                mc_df = (1+get_macro_field(country, "Banks_INDEX", start)).cumprod()
+                df = pd.concat([df, mc_df], axis=1).ffill(axis=0)
+        df = df.iloc[len(df)-252:]
+        # st.write(df.iloc[0])
+        # st.write(df.iloc[-1])
+        
+        st.line_chart(df/df.iloc[0]) #/df.iloc[0]-1)
+
+import json
+
+def save_index(list_assets, titulo):
+    with open('Data/index.json', 'r') as json_file:
+        json_object = json.load(json_file)
+        
+
+    json_object[titulo] = list_assets
+    with open('Data/index.json', 'w') as outfile:
+        json.dump(json_object, outfile)
+    outfile.close()
+
+@st.experimental_memo
+def read_scoring():
+    key = creds3["S3_KEY_ID"]
+    secret_key = creds3["S3_SECRET_KEY"]
+    bucket = creds3["S3_BUCKET"]
+    path ="scoring.xlsx"
+    scoring = read_excel_s3(key, secret_key, bucket, path)
+    return scoring
+
+def read_excel_s3(key, secret_key, bucket, path):
+    s3_client = boto3.client('s3', aws_access_key_id = key, aws_secret_access_key= secret_key)
+    response =  s3_client.get_object(Bucket=bucket, Key=path)
+    data = response["Body"].read()
+    df = pd.read_excel(io.BytesIO(data), engine='openpyxl')
+    return df
+
+
+def get_table_excel_link(df, name):
+    towrite = io.BytesIO()
+    writer = pd.ExcelWriter(towrite, engine='xlsxwriter')
+    downloaded_file = df.to_excel(writer, encoding='utf-8', index=True, 
+                                  header=True)
+    workbook = writer.book
+    worksheet = writer.sheets["Sheet1"]
+    #set the column width as per your requirement
+    worksheet.set_column('A:BZ', 18)
+    writer.save()
+    towrite.seek(0)  # reset pointer
+    file_name = name+'.xlsx'
+    style = 'style="color:black;text-decoration: none; font-size:18px;" '
+    name_mark = name
+    b64 = base64.b64encode(towrite.read()).decode()  # some strings
+    linko = f'<center><a href="data:application/vnd.openxmlformats-officedocument.spreadsheetml.sheet;base64,{b64}" '+style+'download="'+file_name+'"><button>'+name_mark+'</button></a></center>'
+    return linko
+
+
+
+
+
+
+def index_constructor():
+    try:
+        company_base_df = pd.read_excel("Data/Company_Base_Definitivo.xlsx",
+                                        sheet_name='Compilado')
+        scoring = read_scoring()[["Ticker", "Large/Small", "Market_Cap", "ADTV"]]
+        company_base_df = company_base_df.merge(scoring, how='left', on='Ticker')
+        col1, col2, col3, col4 = st.columns(4)
+        country = col1.selectbox("Country",["All", "Chile", "Brazil", "Mexico", "Peru", "Colombia"])
+        large_small = col2.selectbox("Large/Small", ["All", "Large", "Small"])
+        start = col3.text_input('Date', '2022-01')
+        field1 = col4.selectbox("Field", ['IQ_CLOSEPRICE_ADJ', 'IQ_PBV'])
+        if col1.checkbox("Filtro por Mkt Cap"):
+            mkt_cap = col2.number_input("Mkt Cap Min", value=1000)
+            company_base_df = company_base_df[company_base_df["Market_Cap"]>mkt_cap]
+        if col3.checkbox("Filtro por ADTV"):
+            adtv = col4.number_input("ADTV Min", value=1)
+            company_base_df = company_base_df[company_base_df["ADTV"]>adtv]
+        if country != "All":
+            company_base_df = company_base_df[company_base_df["Portfolio_Country"]==country]
+        if large_small != "All":
+            company_base_df = company_base_df[company_base_df["Large/Small"]==large_small] 
+        if st.checkbox("Seleccionar todos"):
+            tickers = st.multiselect("Seleccionar Empresa",
+                                     company_base_df["Ticker"],
+                                     company_base_df["Ticker"])
+        else:
+            tickers = st.multiselect("Seleccionasr Empresa2",
+                                     company_base_df["Ticker"],)
+        if len(tickers)> 0:
+            titulo = col1.text_input("Titulo")
+            save_index = col2.button("Save Index")
+            if save_index:
+                save_index(tickers, titulo)
+            companies_id_dict = dict(zip(company_base_df["Ticker"],
+                                         company_base_df["ID_Quant"]))
+            id_company_dict = dict(zip(company_base_df["ID_Quant"],
+                                       company_base_df["Ticker"]))
+            id_quants = [str(companies_id_dict[ticker]) for ticker in tickers]
+    
+            field = get_asset_field(id_quants,
+                                    field1,
+                                    start,
+                                    expand=False,
+                                    rename=['asset'])
+            ccy = tables.MacroMaster(instrument='FX_USD',
+                                     currency='CLP').query(start=start)
+            if field1 == 'IQ_CLOSEPRICE_ADJ':
+                rets = field.pct_change() # field.mul(ccy, axis=0).pct_change()
+            else:
+                rets = field.ffill(0)
+            mkt_cap = get_asset_field(id_quants,
+                                      'IQ_MARKETCAP',
+                                      start,
+                                      expand=False,
+                                      rename=['asset']).ffill(0)
+            weights = mkt_cap.div(mkt_cap.sum(axis=1), axis=0).shift(1)
+            
+            if field1 == 'IQ_CLOSEPRICE_ADJ':
+                st.line_chart((1 +(rets * weights).sum(axis=1)).cumprod()-1)
+                bm = (1 +(rets * weights).sum(axis=1)).cumprod()-1
+            else:
+                st.line_chart((rets * weights).sum(axis=1))
+                bm =(rets * weights).sum(axis=1)
+            company_id_dict = dict(zip(company_base_df["Ticker"],
+                                       company_base_df["ID_Quant"]))
+            id_company_dict = dict(zip(company_base_df["ID_Quant"],
+                                       company_base_df["Ticker CIQ"]))
+            weights.columns = [id_company_dict[int(col)] for col in weights.columns]
+            rets.columns = [id_company_dict[int(col)] for col in rets.columns]
+            index = (1+get_macro_field('Chile', "INDEX", start)).cumprod()
+            col1, col2, col3, col4 = st.columns(4)
+            col1.markdown(get_table_excel_link(index, "Index"),
+                          unsafe_allow_html=True)
+            col2.markdown(get_table_excel_link(weights, "Weights"),
+                          unsafe_allow_html=True)
+            col3.markdown(get_table_excel_link(rets, "Retornos"),
+                          unsafe_allow_html=True)
+            col4.markdown(get_table_excel_link(bm, "bm"), unsafe_allow_html=True)
+            
+    except Exception as exc:
+        st.write(exc)
+
+
+def pca(rets):
+    from sklearn.decomposition import PCA
+    import numpy as np
+    st.header('PCA')
+    pca = PCA(n_components=10)
+    rets_arr = np.array(rets.fillna(0))
+    rets_df = pd.DataFrame(rets_arr, columns = rets.columns, index= rets.index)
+    st.subheader('Retornos')
+    st.write(rets_df)
+    retorno_factores_arr = pca.fit_transform(rets_arr)
+    weights = pd.DataFrame(pca.components_, columns = rets.columns)
+    st.subheader('Weights')
+    st.write(weights)
+    ret_factor_fin = pd.DataFrame(retorno_factores_arr, index= rets.index)
+    st.subheader('Retornos Factores')
+    st.write(ret_factor_fin)
+    col1, col2 = st.columns(2)
+    st.write(pca.explained_variance_ratio_)
+    st.write(pca.explained_variance_ratio_.cumsum())
+    
+    col1.markdown(get_table_excel_link(weights, "Weights"),
+                  unsafe_allow_html=True)
+    col2.markdown(get_table_excel_link(ret_factor_fin, "Retornos PCA"),
+                  unsafe_allow_html=True)