AmineTMIRI/backend
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

remove esg endpoints

Browse Source
This commit is contained in:
MuslemRahimi 2024-08-31 22:03:41 +02:00
parent 7a19524d59
commit 56910ab841
3 changed files with 20 additions and 55 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

38
app/create_stock_db.py
View File

@ -100,17 +100,17 @@ class StockDatabase:
urls = [ urls = [
f"https://financialmodelingprep.com/api/v3/profile/{symbol}?apikey={api_key}", f"https://financialmodelingprep.com/api/v3/profile/{symbol}?apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/quote/{symbol}?apikey={api_key}", f"https://financialmodelingprep.com/api/v3/quote/{symbol}?apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/income-statement/{symbol}?period=quarter&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/income-statement/{symbol}?period=annual&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/income-statement-growth/{symbol}?period=quarter&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/income-statement-growth/{symbol}?period=annual&apikey={api_key}",
f"https://financialmodelingprep.com/api/v4/esg-environmental-social-governance-data-ratings?symbol={symbol}&apikey={api_key}", #f"https://financialmodelingprep.com/api/v4/esg-environmental-social-governance-data-ratings?symbol={symbol}&apikey={api_key}",
f"https://financialmodelingprep.com/api/v4/esg-environmental-social-governance-data?symbol={symbol}&apikey={api_key}", #f"https://financialmodelingprep.com/api/v4/esg-environmental-social-governance-data?symbol={symbol}&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/historical-price-full/stock_dividend/{symbol}?limit=400&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/historical-price-full/stock_dividend/{symbol}?limit=400&apikey={api_key}",
f"https://financialmodelingprep.com/api/v4/historical/employee_count?symbol={symbol}&apikey={api_key}", f"https://financialmodelingprep.com/api/v4/historical/employee_count?symbol={symbol}&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/balance-sheet-statement/{symbol}?period=quarter&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/balance-sheet-statement/{symbol}?period=annual&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/balance-sheet-statement-growth/{symbol}?period=quarter&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/balance-sheet-statement-growth/{symbol}?period=annual&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/cash-flow-statement/{symbol}?period=quarter&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/cash-flow-statement/{symbol}?period=annual&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/cash-flow-statement-growth/{symbol}?period=quarter&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/cash-flow-statement-growth/{symbol}?period=annual&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/ratios/{symbol}?period=quarter&apikey={api_key}", f"https://financialmodelingprep.com/api/v3/ratios/{symbol}?period=annual&apikey={api_key}",
f"https://financialmodelingprep.com/api/v3/historical-price-full/stock_split/{symbol}?apikey={api_key}", f"https://financialmodelingprep.com/api/v3/historical-price-full/stock_split/{symbol}?apikey={api_key}",
f"https://financialmodelingprep.com/api/v4/stock_peers?symbol={symbol}&apikey={api_key}", f"https://financialmodelingprep.com/api/v4/stock_peers?symbol={symbol}&apikey={api_key}",
f"https://financialmodelingprep.com/api/v4/institutional-ownership/institutional-holders/symbol-ownership-percent?date={quarter_date}&symbol={symbol}&page=0&apikey={api_key}", f"https://financialmodelingprep.com/api/v4/institutional-ownership/institutional-holders/symbol-ownership-percent?date={quarter_date}&symbol={symbol}&page=0&apikey={api_key}",
@ -124,7 +124,7 @@ class StockDatabase:
# Check if 'income' and 'income_growth' data already exist for the symbol # Check if 'income' and 'income_growth' data already exist for the symbol
try: try:
self.cursor.execute("SELECT income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios, stock_peers, esg_data, esg_ratings FROM stocks WHERE symbol = ?", (symbol,)) self.cursor.execute("SELECT income, income_growth, balance, balance_growth, cashflow, cashflow_growth, ratios, stock_peersFROM stocks WHERE symbol = ?", (symbol,))
existing_data = self.cursor.fetchone() existing_data = self.cursor.fetchone()
income_exists = existing_data and existing_data[0] is not None income_exists = existing_data and existing_data[0] is not None
income_growth_exists = existing_data and existing_data[1] is not None income_growth_exists = existing_data and existing_data[1] is not None
@ -176,12 +176,7 @@ class StockDatabase:
elif 'stock_peers/' in url and stock_peers_exists: elif 'stock_peers/' in url and stock_peers_exists:
print(f"Skipping stock_peers for {symbol} as it already exists.") print(f"Skipping stock_peers for {symbol} as it already exists.")
continue continue
elif 'esg-environmental-social-governance-data' in url and esg_data_exists:
print(f"Skipping esg_data for {symbol} as it already exists.")
continue
elif 'esg-environmental-social-governance-data-ratings?' in url and esg_ratings_exists:
print(f"Skipping esg_ratings for {symbol} as it already exists.")
continue
async with session.get(url) as response: async with session.get(url) as response:
@ -284,16 +279,7 @@ class StockDatabase:
elif isinstance(parsed_data, list) and "cash-flow-statement-growth/" in url: elif isinstance(parsed_data, list) and "cash-flow-statement-growth/" in url:
# Handle list response, save as JSON object # Handle list response, save as JSON object
fundamental_data['cashflow_growth'] = ujson.dumps(parsed_data) fundamental_data['cashflow_growth'] = ujson.dumps(parsed_data)
elif isinstance(parsed_data, list) and "esg-environmental-social-governance-data?" in url:
# Handle list response, save as JSON object
fundamental_data['esg_data'] = ujson.dumps(parsed_data[0])
data_dict = {'ESGScore': parsed_data[0]['ESGScore']}
fundamental_data.update(data_dict)
elif isinstance(parsed_data, list) and "esg-environmental-social-governance-data-ratings?" in url:
# Handle list response, save as JSON object
fundamental_data['esg_ratings'] = ujson.dumps(parsed_data[0])
elif "stock_dividend" in url: elif "stock_dividend" in url:
# Handle list response, save as JSON object # Handle list response, save as JSON object
fundamental_data['stock_dividend'] = ujson.dumps(parsed_data) fundamental_data['stock_dividend'] = ujson.dumps(parsed_data)

26
app/cron_stockdeck.py
View File

@ -58,16 +58,6 @@ async def get_data(ticker):
except: except:
forward_pe = None forward_pe = None
if data['esg_data'] == None:
company_esg_score = {'ESGScore': 'n/a', 'socialScore': 'n/a', 'environmentalScore': 'n/a', 'governanceScore': 'n/a'}
company_esg_rating = {'ESGRiskRating': 'n/a', 'industry': 'n/a'}
else:
company_esg_score = ujson.loads(data['esg_data'])
if data['esg_ratings'] == None:
company_esg_rating = {'ESGRiskRating': 'n/a', 'industry': 'n/a'}
else:
company_esg_rating = ujson.loads(data['esg_ratings'])
if data['stock_split'] == None: if data['stock_split'] == None:
company_stock_split = [] company_stock_split = []
else: else:
@ -92,28 +82,12 @@ async def get_data(ticker):
'previousClose': company_quote['price'], #This is true because I update my db before the market opens hence the price will be the previousClose price. 'previousClose': company_quote['price'], #This is true because I update my db before the market opens hence the price will be the previousClose price.
'website': company_profile[0]['website'], 'website': company_profile[0]['website'],
'description': company_profile[0]['description'], 'description': company_profile[0]['description'],
'esgScore': company_esg_score['ESGScore'],
'socialScore': company_esg_score['socialScore'],
'environmentalScore': company_esg_score['environmentalScore'],
'governanceScore': company_esg_score['governanceScore'],
'esgRiskRating': company_esg_rating['ESGRiskRating'],
'fullTimeEmployees': company_profile[0]['fullTimeEmployees'], 'fullTimeEmployees': company_profile[0]['fullTimeEmployees'],
'stockSplits': company_stock_split, 'stockSplits': company_stock_split,
} }
] ]
if data['esg_data'] == None:
company_esg_score = {'ESGScore': 'n/a', 'socialScore': 'n/a', 'environmentalScore': 'n/a', 'governanceScore': 'n/a'}
company_esg_rating = {'ESGRiskRating': 'n/a', 'industry': 'n/a'}
else:
company_esg_score = ujson.loads(data['esg_data'])
if data['esg_ratings'] == None:
company_esg_rating = {'ESGRiskRating': 'n/a', 'industry': 'n/a'}
else:
company_esg_rating = ujson.loads(data['esg_ratings'])
final_res = {k: v for d in [res_profile] for dict in d for k, v in dict.items()} final_res = {k: v for d in [res_profile] for dict in d for k, v in dict.items()}
return final_res return final_res

11
app/restart_json.py
View File

@ -56,7 +56,7 @@ async def get_stock_screener(con,symbols):
#Stock Screener Data #Stock Screener Data
cursor.execute("SELECT symbol, name, avgVolume, change_1W, change_1M, change_3M, change_6M, change_1Y, change_3Y, sma_50, sma_200, ema_50, ema_200, rsi, atr, stoch_rsi, mfi, cci, priceToSalesRatio, priceToBookRatio, eps, pe, ESGScore, marketCap, revenue, netIncome, grossProfit, costOfRevenue, costAndExpenses, interestIncome, interestExpense, researchAndDevelopmentExpenses, ebitda, operatingExpenses, operatingIncome, growthRevenue, growthNetIncome, growthGrossProfit, growthCostOfRevenue, growthCostAndExpenses, growthInterestExpense, growthResearchAndDevelopmentExpenses, growthEBITDA, growthEPS, growthOperatingExpenses, growthOperatingIncome, beta FROM stocks WHERE eps IS NOT NULL AND revenue IS NOT NULL AND marketCap IS NOT NULL AND beta IS NOT NULL") cursor.execute("SELECT symbol, name, avgVolume, change_1W, change_1M, change_3M, change_6M, change_1Y, change_3Y, sma_50, sma_200, ema_50, ema_200, rsi, atr, stoch_rsi, mfi, cci, priceToSalesRatio, priceToBookRatio, eps, pe, marketCap, revenue, netIncome, grossProfit, costOfRevenue, costAndExpenses, interestIncome, interestExpense, researchAndDevelopmentExpenses, ebitda, operatingExpenses, operatingIncome, growthRevenue, growthNetIncome, growthGrossProfit, growthCostOfRevenue, growthCostAndExpenses, growthInterestExpense, growthResearchAndDevelopmentExpenses, growthEBITDA, growthEPS, growthOperatingExpenses, growthOperatingIncome, beta FROM stocks WHERE eps IS NOT NULL AND revenue IS NOT NULL AND marketCap IS NOT NULL AND beta IS NOT NULL")
raw_data = cursor.fetchall() raw_data = cursor.fetchall()
stock_screener_data = [{ stock_screener_data = [{
'symbol': symbol, 'symbol': symbol,
@ -81,7 +81,6 @@ async def get_stock_screener(con,symbols):
'priceToBookRatio': priceToBookRatio, 'priceToBookRatio': priceToBookRatio,
'eps': eps, 'eps': eps,
'pe': pe, 'pe': pe,
'esgScore': ESGScore,
'marketCap': marketCap, 'marketCap': marketCap,
'revenue': revenue, 'revenue': revenue,
'netIncome': netIncome, 'netIncome': netIncome,
@ -106,7 +105,7 @@ async def get_stock_screener(con,symbols):
'growthOperatingExpenses': growthOperatingExpenses, 'growthOperatingExpenses': growthOperatingExpenses,
'growthOperatingIncome': growthOperatingIncome, 'growthOperatingIncome': growthOperatingIncome,
'beta': beta, 'beta': beta,
} for (symbol, name, avgVolume, change_1W, change_1M, change_3M, change_6M, change_1Y, change_3Y, sma_50, sma_200, ema_50, ema_200, rsi, atr, stoch_rsi, mfi, cci, priceToSalesRatio, priceToBookRatio, eps, pe, ESGScore, marketCap, revenue, netIncome, grossProfit,costOfRevenue, costAndExpenses, interestIncome, interestExpense, researchAndDevelopmentExpenses, ebitda, operatingExpenses, operatingIncome, growthRevenue, growthNetIncome, growthGrossProfit, growthCostOfRevenue, growthCostAndExpenses, growthInterestExpense, growthResearchAndDevelopmentExpenses, growthEBITDA, growthEPS, growthOperatingExpenses, growthOperatingIncome, beta) in raw_data] } for (symbol, name, avgVolume, change_1W, change_1M, change_3M, change_6M, change_1Y, change_3Y, sma_50, sma_200, ema_50, ema_200, rsi, atr, stoch_rsi, mfi, cci, priceToSalesRatio, priceToBookRatio, eps, pe, marketCap, revenue, netIncome, grossProfit,costOfRevenue, costAndExpenses, interestIncome, interestExpense, researchAndDevelopmentExpenses, ebitda, operatingExpenses, operatingIncome, growthRevenue, growthNetIncome, growthGrossProfit, growthCostOfRevenue, growthCostAndExpenses, growthInterestExpense, growthResearchAndDevelopmentExpenses, growthEBITDA, growthEPS, growthOperatingExpenses, growthOperatingIncome, beta) in raw_data]
stock_screener_data = [{k: round(v, 2) if isinstance(v, (int, float)) else v for k, v in entry.items()} for entry in stock_screener_data] stock_screener_data = [{k: round(v, 2) if isinstance(v, (int, float)) else v for k, v in entry.items()} for entry in stock_screener_data]
@ -203,6 +202,9 @@ async def get_stock_screener(con,symbols):
item['debtEquityRatio'] = round(float(res['debtEquityRatio']),2) item['debtEquityRatio'] = round(float(res['debtEquityRatio']),2)
item['quickRatio'] = round(float(res['quickRatio']),2) item['quickRatio'] = round(float(res['quickRatio']),2)
item['currentRatio'] = round(float(res['currentRatio']),2) item['currentRatio'] = round(float(res['currentRatio']),2)
item['freeCashFlowPerShare'] = round(float(res['freeCashFlowPerShare']),2)
item['cashPerShare'] = round(float(res['cashPerShare']),2)
item['priceToFreeCashFlowsRatio'] = round(float(res['priceToFreeCashFlowsRatio']),2)
except: except:
item['returnOnAssets'] = None item['returnOnAssets'] = None
@ -211,6 +213,9 @@ async def get_stock_screener(con,symbols):
item['debtEquityRatio'] = None item['debtEquityRatio'] = None
item['quickRatio'] = None item['quickRatio'] = None
item['currentRatio'] = None item['currentRatio'] = None
item['freeCashFlowPerShare'] = None
item['cashPerShare'] = None
item['priceToFreeCashFlowsRatio'] = None
return stock_screener_data return stock_screener_data