fixed quarters

Elaborated_Data/employees_over_time.csv

@@ -1,91 +1,91 @@
 ,Ticker,Employees_over_time
-0,AAPL,"[37376.653472053375, 46285.99144811846, 51441.24426379464, 61558.083471658676, 58907.38311263634, 67601.33974642337, 78133.03145013178, 90447.36485241847, 105018.28649449686, 129688.03180312812, 156569.24938667504, 164000]"
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 15,CAT,"[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0]"
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Elaborated_Data/normalized_data.csv

@@ -1,91 +1,91 @@
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+64,64,NVDA,82.25461700620143,96.42166681686537,164.85,6.926609061624467
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+69,69,PM,109.50100591046719,117.45318957281012,108.25,48.18188763164932
+70,70,PYPL,151.48799177385294,89.77722451354893,146.26,60.19328753757178
+71,71,SCHW,129.0234669420565,86.63564172931416,119.99,45.70354212187421
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+75,75,T,61.586410114552315,100.96624467499457,69.11,59.731454906145466
+76,76,TGT,146.61361867919015,99.71524228634202,41.98,28.280252582031757
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+78,78,TMUS,155.01159044367853,107.0127618105869,199.49,58.89699852799944
+79,79,TSLA,129.88191763420065,84.96764637366414,124.08,62.575536448354775
+80,80,TXN,95.34312077123577,99.06053996559143,120.95,61.63351739609634
+81,81,UNH,130.59286903995337,94.66849120588603,134.93,50.071745102527345
+82,82,UNP,102.08194911395603,107.3396428258993,116.17,39.50929713815176
+83,83,UPS,109.31516556780723,99.91974057004741,89.41,42.27357028519867
+84,84,USB,142.45442985947,90.02273556457382,90.49,41.1838844914599
+85,85,V,132.2376969692613,95.28520596906688,141.79,61.08058325638432
+86,86,VZ,25.824157206441292,104.70865363132539,70.84,35.57825810344986
+87,87,WFC,147.69065957371842,89.57452016283523,99.67,19.075069084803722
+88,88,WMT,80.94310440425323,95.19788921696505,98.07,80.66162547687941
+89,89,XOM,125.08994915257153,88.56559789350317,32.26,74.58447727917255

backend/api/assets_debts.py

@@ -10,12 +10,18 @@ def compare_balance_sheets(ticker_list: list):
 
     for i, ticker in enumerate(ticker_list):
         assets_debt = pd.read_csv(ROOT_PATH + r'/Companies_Data/' + ticker + '_Data/' + ticker + '_balance_sheet_4Y+4Q.csv')
-        selected_data = assets_debt[['TotalAssets', 'TotalDebt', 'CurrentAssets', 'CurrentDebt']]
+        selected_data = assets_debt
 
-        df_ret.loc[ticker, 'total_assets'] = selected_data.iloc[0]['TotalAssets']
-        df_ret.loc[ticker, 'current_assets'] = selected_data.iloc[0]['TotalDebt']
-        df_ret.loc[ticker, 'total_debt'] = selected_data.iloc[0]['CurrentAssets']
-        df_ret.loc[ticker, 'current_debt'] = selected_data.iloc[0]['CurrentDebt']
+        def get_or_na(column: str): 
+            row = selected_data.iloc[-2]
+            if column in row and not np.isnan(row[column]):
+                return row[column]
+            return 0
+
+        df_ret.loc[ticker, 'total_assets'] = get_or_na('TotalAssets')
+        df_ret.loc[ticker, 'current_assets'] = get_or_na('CurrentAssets')
+        df_ret.loc[ticker, 'total_debt'] = get_or_na('TotalDebt')
+        df_ret.loc[ticker, 'current_debt'] = get_or_na('CurrentDebt')
 
     df_ret.loc[:, 'old_assets'] = df_ret['total_assets'].copy() - df_ret['current_assets'].copy()
     df_ret.loc[:, 'old_debt'] = df_ret['total_debt'].copy() - df_ret['current_debt'].copy()

backend/api/eps.py

@@ -3,15 +3,56 @@ import pandas as pd
 import numpy as np
 from scraper.top100_extractor import programming_crime_list
 from typing import Optional
+from dateutil.parser import isoparse
 
 DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborated_Data', 'eps_quarterly_bar_chart.csv')
 
+def ticker_quarter_list(df_ticker: pd.Series) -> list[int]:
+    dfu = df_ticker.unique()
+    months = set([isoparse(date).month for date in dfu])
+    return list(sorted(months))
+
 
 def get_eps(tickers: list[str]) -> list[dict]:
     df = pd.read_csv(DF_EPS_PATH)
     ticker_series = pd.Series(tickers)
+    qmap = dict([ \
+        [ticker, ticker_quarter_list(df.loc[df.symbol == ticker, 'quarter'])] \
+        for ticker in tickers \
+    ])
+
     df = df.loc[df.symbol.isin(ticker_series), :] \
         .rename(columns={"epsActual": "quarterlyEps", "symbol": "ticker"}) \
         .reset_index(drop=True)
+
+    def get_quart(x):
+        date = isoparse(x.quarter)
+        quarter = qmap[x.ticker].index(date.month) + 1
+        return f"{date.year}-Q{quarter}"
+
+    df['quarter'] = df.apply(get_quart, axis=1)
     df = df.pivot(index='quarter', columns='ticker', values='quarterlyEps').reset_index(drop=False)
     return df.replace({ np.nan: None }).to_dict('records')
+
+
+def get_eps_old(tickers: list[str]) -> list[dict]:
+    eps_df = pd.read_csv('Elaborated_Data/eps_quarterly_bar_chart.csv', index_col=[0])
+
+    s_tickers = pd.Series(tickers)
+    eps_df = eps_df.loc[eps_df['symbol'].isin(s_tickers), :]
+
+    dates = eps_df['quarter'].to_list()
+
+    for i, date in enumerate(dates):
+        eps_df[date] = eps_df['epsActual'].copy().apply(lambda x: int(eval(str(x))[i]))
+
+    eps_df = pd.melt(eps_df, id_vars=['symbol'], value_vars=['quarter'], var_name='quarter').reset_index(drop=True)
+    print(eps_df)
+    eps_df = eps_df.pivot(index='value', columns='symbol', values='value').reset_index(drop=False)
+    exit(1)
+    for ticker in tickers:
+        filepath = f'Companies_Data/{ticker}_Data/{ticker}earnings.csv'
+        eps_quarterly = pd.read_csv(filepath)
+        ticker_df = eps_quarterly.loc[eps_quarterly['symbol'] == ticker]
+        ticker_df['ticker'] = ticker
+        combined_df = combined_df._append(ticker_df)

backend/api/past_performance.py

@@ -3,6 +3,8 @@ import pandas as pd
 import numpy as np
 from scraper.top100_extractor import programming_crime_list
 from typing import Optional
+from backend.api.eps import ticker_quarter_list
+from dateutil.parser import isoparse
 
 DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborated_Data', 'eps_comparison.csv')
 
@@ -10,8 +12,23 @@ DF_EPS_PATH: str = os.path.join(os.path.dirname(__file__), '..', '..', 'Elaborat
 def get_eps_comp(tickers: list[str]) -> list[dict]:
     df = pd.read_csv(DF_EPS_PATH)
     ticker_series = pd.Series(tickers)
+
     df = df.loc[df['Ticker'].isin(ticker_series), :] \
         .rename(columns={"epsDifferential": "quarterlyDifferential", "Ticker": "ticker"}) \
         .reset_index(drop=True)
+
+    qmap = dict([ \
+        [ticker, ticker_quarter_list(df.loc[df.ticker == ticker, 'quarter'])] \
+        for ticker in tickers \
+    ])
+
+    def get_quart(x):
+        date = isoparse(x.quarter)
+        quarter = qmap[x.ticker].index(date.month) + 1
+        return f"{date.year}-Q{quarter}"
+
+    df['quarter'] = df.apply(get_quart, axis=1)
+    
+
     df = df.pivot(index='quarter', columns='ticker', values='quarterlyDifferential').reset_index(drop=False)
     return df.replace({ np.nan: None }).to_dict('records')

indexer/indexer.py

@@ -99,7 +99,7 @@ def normalizer():
     v_values = (200/(1+math.e**( 0.5*(-not_normalized['Valuation'].mean()+not_normalized['Valuation'])))) #VALUATION STAT
     not_normalized['Valuation'] = v_values
 
-    fh_values= (200/(1+math.e**( 0.5*(not_normalized['Financial Health'].mean()+not_normalized['Financial Health'])))) #FINANCIAL HEALTH STAT
+    fh_values= (200/(1+math.e**( 0.9*(not_normalized['Financial Health'].mean()-not_normalized['Financial Health'])))) #FINANCIAL HEALTH STAT
     not_normalized['Financial Health'] = fh_values 
 
     not_normalized['Estimated Growth'] = not_normalized['Estimated Growth'].str.strip("%").astype("float")

requirements.txt

@@ -7,3 +7,4 @@ flask==2.3.2
 itsdangerous==2.1.2
 Flask_Cors==3.0.10
 six==1.16.0
+python-dateutil==2.8.2

stockingly-frontend/src/api/index.ts

@@ -41,7 +41,6 @@ export interface BalanceSheet {
 export interface Eps {
     [ticker: string]: string | number; // really just number
     quarter: string; 
-
 }
 
 export interface EpsComp {

stockingly-frontend/src/components/BalanceSheet.vue

@@ -108,6 +108,7 @@ const options = computed(() => {
           enabled: true,
           text: 'Amount in USD',
         },
+        min: 0,
         type: 'number',
         position: 'left',
         label: {

stockingly-frontend/src/components/EmployeesChart.vue

@@ -80,6 +80,7 @@ const options = computed(() => {
       {
         type: 'number',
         position: 'left',
+        min: 0,
         gridStyle: [{
           lineDash: [Infinity]
         }, {

stockingly-frontend/src/components/EpsChart.vue

@@ -13,14 +13,13 @@
 <script setup lang="ts">
 import { Eps, getEps } from '@/api';
 import { defineLoader } from '@/api/loader';
-import { DateTime } from 'luxon';
 import { roundTo } from 'round-to';
 import { onMounted, computed } from 'vue';
 import { AgChartsVue } from 'ag-charts-vue3';
 
 const renderer = (params: any) => ({
   title: params.title,
-  content: DateTime.fromMillis(params.xValue).toFormat('yyyy-MM') + ': ' + roundTo(params.yValue, 4),
+  content: params.xValue + ': ' + roundTo(params.yValue, 4),
 });
 
 const props = defineProps<{
@@ -37,7 +36,7 @@ const options = computed(() => {
 
   return {
     theme: 'ag-material',
-    data: epsData.data?.map(e => ({ ...e, quarter: Date.parse(e.quarter) })),
+    data: epsData.data ?? [],
     series: props.tickers.map((t: string) => ({
       xKey: 'quarter',
       type: 'column',
@@ -58,9 +57,6 @@ const options = computed(() => {
         label: {
           autoRotate: true,
           autoRotateAngle: 335,
-          formatter: (params: any) => {
-            return DateTime.fromMillis(parseInt(params.value)).toFormat('yyyy-MM');
-          }
         },
         gridStyle: [{
           lineDash: [Infinity]
@@ -75,6 +71,7 @@ const options = computed(() => {
       {
         type: 'number',
         position: 'left',
+        min: 0,
         gridStyle: [{
           lineDash: [Infinity]
         }, {

stockingly-frontend/src/components/EpsComparisonChart.vue

@@ -13,7 +13,6 @@
 <script setup lang="ts">
 import { EpsComp, getEpsComp } from '@/api';
 import { defineLoader } from '@/api/loader';
-import { DateTime } from 'luxon';
 import { AgAxisLabelFormatterParams } from 'ag-charts-community';
 import { roundTo } from 'round-to';
 import { onMounted, computed } from 'vue';
@@ -21,7 +20,7 @@ import { AgChartsVue } from 'ag-charts-vue3';
 
 const renderer = (params: any) => ({
   title: params.title,
-  content: DateTime.fromMillis(params.xValue).toFormat('yyyy-MM') + ': ' + roundTo(params.yValue, 4) + '%',
+  content: params.xValue + ': ' + roundTo(params.yValue, 4) + '%',
 });
 
 const props = defineProps<{
@@ -38,7 +37,7 @@ const options = computed(() => {
 
   return {
     theme: 'ag-material',
-    data: epsCompData.data?.map(e => ({ ...e, quarter: Date.parse(e.quarter) })),
+    data: epsCompData.data?.map(e => ({ ...e })),
     series: props.tickers.map((t: string) => ({
       xKey: 'quarter',
       type: 'line',
@@ -60,11 +59,7 @@ const options = computed(() => {
         type: 'category',
         position: 'bottom',
         label: {
-          autoRotate: true,
-          autoRotateAngle: 335,
-          formatter: (params: any) => {
-            return DateTime.fromMillis(parseInt(params.value)).toFormat('yyyy-MM');
-          }
+          rotation: 315,
         },
         gridStyle: [
           {
@@ -84,6 +79,8 @@ const options = computed(() => {
             return params.value + '%';
           }
         },
+        min: Math.min(0, ...epsCompData.data?.flatMap(e => Object.values(e)
+          .filter((e: string | number): e is number => typeof e === 'number')) ?? []),
         gridStyle: [{
           lineDash: [Infinity],
         }, {

stockingly-frontend/src/components/StockPrice.vue

@@ -186,6 +186,7 @@ const options = computed(() => {
       {
         type: 'number',
         position: 'left',
+        min: 0,
         label: {
           format: '$~s',
           formatter: (params: AgAxisLabelFormatterParams) =>