update market movers
This commit is contained in:
MuslemRahimi
parent
59ab55c8cf
commit
15da925dc4
@ -82,7 +82,12 @@ async def get_quote_of_stocks(ticker_list):
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df = await response.json()
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return df
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def add_rank(data):
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for key in data:
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for index, item in enumerate(data[key], start=1):
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item['rank'] = index
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return data
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async def get_gainer_loser_active_stocks():
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#Database read 1y and 3y data
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@ -218,7 +223,9 @@ async def get_gainer_loser_active_stocks():
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# Iterate through time periods, categories, and symbols
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for time_period in data.keys():
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for category in data[time_period].keys():
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for stock_data in data[time_period][category]:
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# Add rank and process symbols
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for index, stock_data in enumerate(data[time_period][category], start=1):
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stock_data['rank'] = index # Add rank field
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symbol = stock_data["symbol"]
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unique_symbols.add(symbol)
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@ -230,16 +237,17 @@ async def get_gainer_loser_active_stocks():
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latest_quote = await get_quote_of_stocks(unique_symbols_list)
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# Updating values in the data list based on matching symbols from the quote list
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for time_period in data.keys():
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for category in data[time_period].keys():
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for stock_data in data[time_period][category]:
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symbol = stock_data["symbol"]
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quote_stock = next((item for item in latest_quote if item["symbol"] == symbol), None)
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if quote_stock:
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stock_data['price'] = quote_stock['price']
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stock_data['changesPercentage'] = quote_stock['changesPercentage']
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stock_data['marketCap'] = quote_stock['marketCap']
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stock_data['volume'] = quote_stock['volume']
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# Only proceed if the time period is "1D"
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if time_period == "1D":
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for category in data[time_period].keys():
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for stock_data in data[time_period][category]:
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symbol = stock_data["symbol"]
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quote_stock = next((item for item in latest_quote if item["symbol"] == symbol), None)
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if quote_stock:
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stock_data['price'] = quote_stock['price']
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stock_data['changesPercentage'] = quote_stock['changesPercentage']
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stock_data['marketCap'] = quote_stock['marketCap']
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stock_data['volume'] = quote_stock['volume']
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return data
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@ -310,17 +318,20 @@ try:
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#Filter out tickers
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symbols = [symbol for symbol in symbols if symbol != "STEC"]
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data = asyncio.run(get_historical_data())
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with open(f"json/mini-plots-index/data.json", 'w') as file:
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ujson.dump(data, file)
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data = asyncio.run(get_gainer_loser_active_stocks())
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with open(f"json/market-movers/data.json", 'w') as file:
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ujson.dump(data, file)
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'''
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data = asyncio.run(get_historical_data())
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with open(f"json/mini-plots-index/data.json", 'w') as file:
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ujson.dump(data, file)
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data = asyncio.run(get_pre_post_market_movers(symbols))
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with open(f"json/market-movers/pre-post-data.json", 'w') as file:
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ujson.dump(data, file)
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'''
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con.close()
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except Exception as e:
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309
app/market_movers.py
Executable file → Normal file
309
app/market_movers.py
Executable file → Normal file
@ -1,152 +1,157 @@
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import sqlite3
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from datetime import datetime, timedelta
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import pandas as pd
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import json
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import time
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class Past_Market_Movers:
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def __init__(self):
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self.con = sqlite3.connect('backup_db/stocks.db')
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self.cursor = self.con.cursor()
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self.cursor.execute("PRAGMA journal_mode = wal")
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self.symbols = self.get_stock_symbols()
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def get_stock_symbols(self):
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self.cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol != ?", ('%5EGSPC',))
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return [row[0] for row in self.cursor.fetchall()]
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@staticmethod
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def check_if_holiday():
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holidays = {
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datetime(2023, 5, 29): 'memorial_day',
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datetime(2023, 6, 19): 'independence_day',
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datetime(2023, 6, 20): 'independence_day+1',
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datetime(2023, 9, 4): 'labor_day',
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}
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current_datetime = datetime.today()
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for holiday_date, holiday_name in holidays.items():
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if current_datetime == holiday_date:
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return holiday_name
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return None
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def correct_weekday_interval(self, prev_day):
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holiday = self.check_if_holiday()
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if holiday:
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if holiday == 'memorial_day':
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start_date = datetime(2023, 5, 26)
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elif holiday in ('independence_day', 'independence_day+1'):
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start_date = datetime(2023, 6, 16)
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else:
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start_date = datetime(2023, 9, 1)
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else:
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current_date = datetime.today() - timedelta(prev_day)
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current_weekday = current_date.weekday()
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if current_weekday in (5, 6): # Saturday or Sunday
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start_date = current_date - timedelta(days=current_weekday % 5 + 1)
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else:
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start_date = current_date
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return start_date.strftime("%Y-%m-%d")
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def run(self, time_periods=[7,30,90,180]):
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performance_data = []
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query_template = """
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SELECT date, close, volume FROM "{ticker}" WHERE date >= ?
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"""
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query_fundamental_template = """
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SELECT marketCap, name FROM stocks WHERE symbol = ?
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"""
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gainer_json = {}
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loser_json = {}
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active_json = {}
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for time_period in time_periods:
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performance_data = []
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high_volume = []
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gainer_data = []
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loser_data = []
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active_data = []
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start_date = self.correct_weekday_interval(time_period)
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for ticker in self.symbols:
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try:
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query = query_template.format(ticker=ticker)
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df = pd.read_sql_query(query, self.con, params=(start_date,))
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if not df.empty:
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fundamental_data = pd.read_sql_query(query_fundamental_template, self.con, params=(ticker,))
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avg_volume = df['volume'].mean()
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if avg_volume > 1E6 and df['close'].mean() > 1:
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changes_percentage = ((df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0]) * 100
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performance_data.append((ticker, fundamental_data['name'].iloc[0], df['close'].iloc[-1], changes_percentage, avg_volume, int(fundamental_data['marketCap'].iloc[0])))
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except:
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pass
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# Sort the stocks by percentage change in descending order
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performance_data.sort(key=lambda x: x[3], reverse=True)
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high_volume = sorted(performance_data, key=lambda x: x[4], reverse=True)
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for symbol, name, price, changes_percentage, volume, market_cap in performance_data[:20]:
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gainer_data.append({'symbol': symbol, 'name': name, 'price': price, 'changesPercentage': changes_percentage, 'volume': volume, 'marketCap': market_cap})
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for symbol, name, price, changes_percentage, volume, market_cap in performance_data[-20:]:
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loser_data.append({'symbol': symbol, 'name': name, 'price': price, 'changesPercentage': changes_percentage, 'volume': volume, 'marketCap': market_cap})
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for symbol, name, price, changes_percentage, volume, market_cap in high_volume[:20]:
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active_data.append({'symbol': symbol, 'name': name, 'price': price, 'changesPercentage': changes_percentage, 'volume': volume, 'marketCap': market_cap})
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if time_period == 7:
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gainer_json['1W'] = gainer_data
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loser_json['1W'] = loser_data
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active_json['1W'] = active_data
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elif time_period == 30:
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gainer_json['1M'] = gainer_data
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loser_json['1M'] = loser_data
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active_json['1M'] = active_data
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elif time_period == 90:
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gainer_json['3M'] = gainer_data
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loser_json['3M'] = loser_data
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active_json['3M'] = active_data
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elif time_period == 180:
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gainer_json['6M'] = gainer_data
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loser_json['6M'] = loser_data
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active_json['6M'] = active_data
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return gainer_json, loser_json, active_json
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def create_table(self):
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"""
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Create the 'market_movers' table if it doesn't exist and add 'gainer', 'loser', and 'most_active' columns.
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"""
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query_drop = "DROP TABLE IF EXISTS market_movers"
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self.con.execute(query_drop)
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query_create = """
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CREATE TABLE IF NOT EXISTS market_movers (
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gainer TEXT,
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loser TEXT,
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most_active TEXT
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)
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"""
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self.con.execute(query_create)
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self.con.commit()
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def update_database(self, gainer_json, loser_json, active_json):
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"""
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Update the 'gainer', 'loser', and 'most_active' columns in the 'market_movers' table with the provided JSON data.
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"""
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query = "INSERT INTO market_movers (gainer, loser, most_active) VALUES (?, ?, ?)"
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gainer_json_str = json.dumps(gainer_json)
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loser_json_str = json.dumps(loser_json)
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active_json_str = json.dumps(active_json)
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self.con.execute(query, (gainer_json_str, loser_json_str, active_json_str))
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self.con.commit()
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def close_database_connection(self):
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self.con.close()
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if __name__ == "__main__":
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analyzer = Past_Market_Movers()
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analyzer.create_table() # Create the 'market_movers' table with the 'gainer', 'loser', and 'most_active' columns
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gainer_json, loser_json, active_json = analyzer.run() # Retrieve the gainer_json, loser_json, and active_json data
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analyzer.update_database(gainer_json, loser_json, active_json) # Update the 'gainer', 'loser', and 'most_active' columns with the respective data
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analyzer.close_database_connection()
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import sqlite3
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from datetime import datetime, timedelta
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import pandas as pd
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import json
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import time
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class Past_Market_Movers:
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def __init__(self):
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self.con = sqlite3.connect('backup_db/stocks.db')
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self.cursor = self.con.cursor()
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self.cursor.execute("PRAGMA journal_mode = wal")
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self.symbols = self.get_stock_symbols()
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def get_stock_symbols(self):
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self.cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol != ?", ('%5EGSPC',))
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return [row[0] for row in self.cursor.fetchall()]
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@staticmethod
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def check_if_holiday():
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holidays = {
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datetime(2023, 5, 29): 'memorial_day',
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datetime(2023, 6, 19): 'independence_day',
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datetime(2023, 6, 20): 'independence_day+1',
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datetime(2023, 9, 4): 'labor_day',
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}
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current_datetime = datetime.today()
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for holiday_date, holiday_name in holidays.items():
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if current_datetime == holiday_date:
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return holiday_name
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return None
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def correct_weekday_interval(self, prev_day):
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holiday = self.check_if_holiday()
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if holiday:
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if holiday == 'memorial_day':
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start_date = datetime(2023, 5, 26)
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elif holiday in ('independence_day', 'independence_day+1'):
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start_date = datetime(2023, 6, 16)
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else:
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start_date = datetime(2023, 9, 1)
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else:
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current_date = datetime.today() - timedelta(prev_day)
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current_weekday = current_date.weekday()
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if current_weekday in (5, 6): # Saturday or Sunday
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start_date = current_date - timedelta(days=current_weekday % 5 + 1)
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else:
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start_date = current_date
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return start_date.strftime("%Y-%m-%d")
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def run(self, time_periods=[7,20,252,756,1260]):
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performance_data = []
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query_template = """
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SELECT date, close, volume FROM "{ticker}" WHERE date >= ?
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"""
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query_fundamental_template = """
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SELECT marketCap, name FROM stocks WHERE symbol = ?
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"""
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gainer_json = {}
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loser_json = {}
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active_json = {}
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for time_period in time_periods:
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performance_data = []
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high_volume = []
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gainer_data = []
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loser_data = []
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active_data = []
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start_date = self.correct_weekday_interval(time_period)
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for ticker in self.symbols:
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try:
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query = query_template.format(ticker=ticker)
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df = pd.read_sql_query(query, self.con, params=(start_date,))
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if not df.empty:
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fundamental_data = pd.read_sql_query(query_fundamental_template, self.con, params=(ticker,))
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avg_volume = df['volume'].mean()
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market_cap = int(fundamental_data['marketCap'].iloc[0])
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if avg_volume > 1E6 and df['close'].mean() > 1 and market_cap >=50E6:
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changes_percentage = ((df['close'].iloc[-1] - df['close'].iloc[0]) / df['close'].iloc[0]) * 100
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performance_data.append((ticker, fundamental_data['name'].iloc[0], df['close'].iloc[-1], changes_percentage, avg_volume, market_cap))
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except:
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pass
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# Sort the stocks by percentage change in descending order
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performance_data.sort(key=lambda x: x[3], reverse=True)
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high_volume = sorted(performance_data, key=lambda x: x[4], reverse=True)
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for symbol, name, price, changes_percentage, volume, market_cap in performance_data[:20]:
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gainer_data.append({'symbol': symbol, 'name': name, 'price': price, 'changesPercentage': changes_percentage, 'volume': volume, 'marketCap': market_cap})
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for symbol, name, price, changes_percentage, volume, market_cap in performance_data[-20:]:
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loser_data.append({'symbol': symbol, 'name': name, 'price': price, 'changesPercentage': changes_percentage, 'volume': volume, 'marketCap': market_cap})
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for symbol, name, price, changes_percentage, volume, market_cap in high_volume[:20]:
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active_data.append({'symbol': symbol, 'name': name, 'price': price, 'changesPercentage': changes_percentage, 'volume': volume, 'marketCap': market_cap})
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if time_period == 7:
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gainer_json['1W'] = gainer_data
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loser_json['1W'] = loser_data
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active_json['1W'] = active_data
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elif time_period == 20:
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gainer_json['1M'] = gainer_data
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loser_json['1M'] = loser_data
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active_json['1M'] = active_data
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elif time_period == 252:
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gainer_json['1Y'] = gainer_data
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loser_json['1Y'] = loser_data
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active_json['1Y'] = active_data
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elif time_period == 756:
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gainer_json['3Y'] = gainer_data
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loser_json['3Y'] = loser_data
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active_json['3Y'] = active_data
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elif time_period == 1260:
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gainer_json['5Y'] = gainer_data
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loser_json['5Y'] = loser_data
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active_json['5Y'] = active_data
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return gainer_json, loser_json, active_json
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def create_table(self):
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"""
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Create the 'market_movers' table if it doesn't exist and add 'gainer', 'loser', and 'most_active' columns.
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"""
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query_drop = "DROP TABLE IF EXISTS market_movers"
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self.con.execute(query_drop)
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query_create = """
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CREATE TABLE IF NOT EXISTS market_movers (
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gainer TEXT,
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loser TEXT,
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most_active TEXT
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)
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"""
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self.con.execute(query_create)
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self.con.commit()
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def update_database(self, gainer_json, loser_json, active_json):
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"""
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Update the 'gainer', 'loser', and 'most_active' columns in the 'market_movers' table with the provided JSON data.
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"""
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query = "INSERT INTO market_movers (gainer, loser, most_active) VALUES (?, ?, ?)"
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gainer_json_str = json.dumps(gainer_json)
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loser_json_str = json.dumps(loser_json)
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active_json_str = json.dumps(active_json)
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self.con.execute(query, (gainer_json_str, loser_json_str, active_json_str))
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self.con.commit()
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def close_database_connection(self):
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self.con.close()
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if __name__ == "__main__":
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analyzer = Past_Market_Movers()
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analyzer.create_table() # Create the 'market_movers' table with the 'gainer', 'loser', and 'most_active' columns
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gainer_json, loser_json, active_json = analyzer.run() # Retrieve the gainer_json, loser_json, and active_json data
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analyzer.update_database(gainer_json, loser_json, active_json) # Update the 'gainer', 'loser', and 'most_active' columns with the respective data
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analyzer.close_database_connection()
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