Use database - paving the road for dynamically adding/deleting watchlist items
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@ -21,7 +21,7 @@ Could also come up with a value that ties to the trading volume.
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import pandas as pd
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import pandas as pd
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import numpy as np
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import numpy as np
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import datetime as dt
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import datetime as dt
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from util import get_watchlist
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# from util import get_watchlist
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from numpy.fft import fft, ifft
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from numpy.fft import fft, ifft
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import scipy.signal as sig
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import scipy.signal as sig
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import plotly.express as px
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import plotly.express as px
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@ -33,8 +33,44 @@ from dash.exceptions import PreventUpdate
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import dash_auth
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import dash_auth
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import yahoo_fin.stock_info as si
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import yahoo_fin.stock_info as si
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import hashlib
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import hashlib
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from dotenv import load_dotenv
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import psycopg2
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import os
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import sys
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pd.options.mode.chained_assignment = None # default='warn'
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pd.options.mode.chained_assignment = None # default='warn'
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load_dotenv()
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def connect_db():
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conn = None
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try:
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conn = psycopg2.connect(
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host=os.environ['DB_PATH'],
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database=os.environ['DB_NAME'],
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user=os.environ['DB_USERNAME'],
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password=os.environ['DB_PASSWORD'],
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)
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except (Exception, psycopg2.DatabaseError) as error:
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print(error)
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sys.exit(1)
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return conn
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def sql_to_dataframe(conn, query):
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cursor = conn.cursor()
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try:
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cursor.execute(query)
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except (Exception, psycopg2.DatabaseError) as error:
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print(f"Error: {error}")
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cursor.close()
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return 1
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tuples_list = cursor.fetchall()
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cursor.close()
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df = pd.DataFrame(tuples_list)
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return df
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def get_watchlist():
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QUERY = '''select * from stock_watch_list'''
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return sql_to_dataframe(connect_db(), QUERY)
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def hash_password(password):
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def hash_password(password):
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# Encode the password as bytes
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# Encode the password as bytes
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@ -485,7 +521,7 @@ def intelligent_loop_plots(sym, stk_data):
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volume_tail = vol.tail(LB_TRIGGER)
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volume_tail = vol.tail(LB_TRIGGER)
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vol_mean = vol.tail(50).mean()
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vol_mean = vol.tail(50).mean()
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vol_std = vol.tail(50).std()
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vol_std = vol.tail(50).std()
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if ((volume_tail[1] - vol_mean - 2*vol_std) > 0).any():
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if ((volume_tail - vol_mean - 2*vol_std) > 0).any():
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# print('--HiVol', end = '')
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# print('--HiVol', end = '')
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plot_indicator += "HiVol, "
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plot_indicator += "HiVol, "
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@ -536,7 +572,8 @@ dash_auth.BasicAuth(
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watchlist = get_watchlist()
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watchlist = get_watchlist()
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# symbols = watchlist.index.values.tolist()
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# symbols = watchlist.index.values.tolist()
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symbols = (watchlist.index.values + " - " + watchlist["Sub Segment"]).tolist()
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# symbols = (watchlist.index.values + " - " + watchlist["Sub Segment"]).tolist()
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symbols = (watchlist.iloc[:, 0] + " - " + watchlist.iloc[:, 1]).tolist()
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CACHE_CONFIG = {'CACHE_TYPE': 'SimpleCache'}
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CACHE_CONFIG = {'CACHE_TYPE': 'SimpleCache'}
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cache = Cache()
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cache = Cache()
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@ -614,7 +651,8 @@ def start_cycle(n, value):
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def reload_syms(n):
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def reload_syms(n):
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if n:
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if n:
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watchlist = get_watchlist()
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watchlist = get_watchlist()
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symbols = (watchlist.index.values + " - " + watchlist["Sub Segment"]).tolist()
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# symbols = (watchlist.index.values + " - " + watchlist["Sub Segment"]).tolist()
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symbols = (watchlist.iloc[:, 0] + " - " + watchlist.iloc[:, 1]).tolist()
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return symbols, 0
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return symbols, 0
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return no_update
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return no_update
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@ -30,10 +30,12 @@ packaging==24.1
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pandas==2.2.3
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pandas==2.2.3
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parse==1.20.2
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parse==1.20.2
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plotly==5.24.1
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plotly==5.24.1
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psycopg2==2.9.9
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pyee==11.1.1
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pyee==11.1.1
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pyppeteer==2.0.0
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pyppeteer==2.0.0
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pyquery==2.0.1
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pyquery==2.0.1
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python-dateutil==2.9.0.post0
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python-dateutil==2.9.0.post0
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python-dotenv==1.0.1
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pytz==2024.2
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pytz==2024.2
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requests==2.32.3
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requests==2.32.3
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requests-html==0.10.0
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requests-html==0.10.0
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384
util.py
384
util.py
@ -1,384 +0,0 @@
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"""
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Use Yahoo Finance data
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"""
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import warnings
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# Suppress FutureWarnings
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warnings.simplefilter(action='ignore', category=FutureWarning)
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import datetime as dt
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import os
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import pandas as pd
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import numpy as np
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# import yfinance as yf
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import yahoo_fin.stock_info as si
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import requests
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from lxml import html
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from io import StringIO
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from time import sleep
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WEBSITE = 'https://www.isolo.org/dokuwiki/knowledge_base/investing/watchlist'
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BATCHSIZE = 20
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TIMEGAP = 0.2
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def fill_missing_data(df):
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temp = df.ffill()
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temp = temp.bfill()
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return temp
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def symbol_to_path(symbol, base_dir=None):
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"""Return CSV file path given ticker symbol."""
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if base_dir is None:
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base_dir = os.environ.get("MARKET_DATA_DIR", '../data/')
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return os.path.join(base_dir, "{}.csv".format(str(symbol)))
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# def get_data_full(symbols, start_date, addSPY=True, colname = 'Adj Close'):
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# """
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# Read stock data (adjusted close) for given symbols from Yahoo Finance
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# from start_date to the latest date available (usually the current date).
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# """
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# if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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# symbols = ['SPY'] + symbols
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# df = yf.download(symbols, start = start_date)[colname]
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# if len(symbols) == 1:
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# df.name = symbols[0]
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# df = df.to_frame()
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# return df
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# def get_data_full(symbols, start_date, addSPY=True, colname = 'Adj Close'):
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"""
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Read stock data (adjusted close) for given symbols from CSV files
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from start_date to the latest date available in the CSV files.
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"""
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# df_temp = pd.read_csv(symbol_to_path('SPY'), index_col='Date',
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# parse_dates=True, usecols=['Date', colname], na_values=['nan'])
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# df_temp = df_temp.rename(columns={colname: 'SPY'})
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# end_date = df_temp.index.values[-1]
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# dates = pd.date_range(start_date, end_date)
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# df = pd.DataFrame(index=dates)
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# df = df.join(df_temp)
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# df = df.dropna()
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# # if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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# # symbols = ['SPY'] + symbols
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# for symbol in symbols:
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# df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
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# parse_dates=True, usecols=['Date', colname], na_values=['nan'])
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# df_temp = df_temp.rename(columns={colname: symbol})
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# df = df.join(df_temp)
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# # if symbol == 'SPY': # drop dates SPY did not trade
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# # df = df.dropna(subset=["SPY"])
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# if not addSPY:
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# df = df[symbols]
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# return df
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def get_data_range(df, dates):
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"""
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Extract sections of the data in the dates range from the full data set
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"""
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df_range = pd.DataFrame(index=dates)
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df_range = df_range.join(df, how='inner')
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return df_range
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def yf_download(symbols, start, end):
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df = pd.DataFrame(columns = pd.MultiIndex(levels=[["Adj Close", "Volume"],[]], codes=[[],[]], names=["param", "tick"]))
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for sym in symbols:
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# tmp = si.get_data(sym, start_date=start)
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tmp = si.get_data(sym, start_date=start)[["adjclose", "volume"]]
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tmp.rename(columns={"adjclose": "Adj Close", "volume": "Volume"}, inplace=True)
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tmp.columns = pd.MultiIndex.from_product([list(tmp.columns)] + [[sym]], names=["param", "tick"])
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df = df.join(tmp, how='outer')
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return df
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# def get_data(symbols, dates, addSPY=True, colname = 'Adj Close'):
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# """
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# Read stock data (adjusted close) for given symbols from Yahoo Finance
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# """
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# org_sym = symbols
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# sd = dates[0]
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# ed = dates[-1]
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# # if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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# if 'SPY' not in symbols:
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# symbols = ['SPY'] + symbols
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# df = yf.download(symbols, start=sd, end = ed)[colname]
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# if len(symbols) == 1:
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# df.name = symbols[0]
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# df = df.to_frame()
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# df = df.dropna(subset=['SPY'])
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# df = fill_missing_data(df)
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# if addSPY==False:
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# # df = df.drop(columns=['SPY'])
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# df = df[org_sym]
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# return df
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def yf_batch_download(symbols, start, end, batch_size, time_gap):
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"""
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download in small batches to avoid connection closure by host
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Parameters
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----------
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symbols : list
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stock symbols.
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start : datetime
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start date.
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end : datetime
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stop date.
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batch_size : integer
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batch size.
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time_gap : float
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in seconds or fraction of seconds.
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Returns
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-------
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df : dataframe
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stock price volume information.
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"""
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n = len(symbols)
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batches = n // batch_size
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df = pd.DataFrame()
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for i in range(batches - 1):
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tmp = yf_download(symbols[i*batch_size:(i+1)*batch_size], start, end)
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df = pd.concat([df, tmp], axis=1)
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sleep(time_gap)
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tmp = yf_download(symbols[(batches-1)*batch_size:n], start, end)
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df = pd.concat([df, tmp], axis=1)
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return df
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def get_price_volume(symbols, dates, addSPY=False):
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"""
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Read stock data (adjusted close and volume) for given symbols from local
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file unless data is not in local. It only gets date from Yahoo Finance
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when necessary to increase speed and reduce internet data.
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It will refresh local data if the symbols are on the _refresh.csv. This
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is necessary when stock splits, spins off or something else happens.
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"""
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# DATAFILE = "_stkdata.pickle"
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# REFRESH = "_refresh.csv"
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org_sym = symbols
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sd = dates[0]
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ed = dates[-1]
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# if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
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if 'SPY' not in symbols:
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symbols = ['SPY'] + symbols
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df = yf_batch_download(symbols, start=sd, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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if len(symbols) == 1:
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tuples = list(zip(df.columns.values.tolist(), \
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[symbols[0]]*len(df.columns.values)))
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df.columns = pd.MultiIndex.from_tuples(tuples, names=[None, None])
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# if not os.path.exists(DATAFILE):
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# df = yf_batch_download(symbols, start=sd, end=ed, \
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# batch_size=BATCHSIZE, time_gap=TIMEGAP)
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# if len(symbols) == 1:
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# tuples = list(zip(df.columns.values.tolist(), \
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# [symbols[0]]*len(df.columns.values)))
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# df.columns = pd.MultiIndex.from_tuples(tuples, names=[None, None])
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# else:
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# df = pd.read_pickle(DATAFILE)
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# exist_syms = df["Adj Close"].columns.values.tolist()
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# if os.path.exists(REFRESH):
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# try:
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# refresh_df = pd.read_csv(REFRESH, header=None)
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# refresh_syms = refresh_df.values.tolist()
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# refresh_syms = [x for sublist in refresh_syms for x in sublist]
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# remove_syms = [x for x in exist_syms if x in refresh_syms]
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# if remove_syms:
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# df.drop(columns=remove_syms, axis=1, level=1, inplace=True)
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# exist_syms = [x for x in exist_syms if x not in refresh_syms]
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# except:
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# pass
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exist_syms = []
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last_day = pd.to_datetime(df.index.values[-1])
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first_day = pd.to_datetime(df.index.values[0])
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intersect_syms = list(set(org_sym) & set(exist_syms))
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# reduce df to only contain intersect_syms
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df = df.loc[:, (slice(None), intersect_syms)]
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if sd < first_day:
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# fill gap from online
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tmp_df = yf_batch_download(intersect_syms, start=sd, end=first_day, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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df = pd.concat([tmp_df, df])
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if ed >= last_day:
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# fill gap from online incl last two days to get mkt close data
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if ed.date() == last_day.date():
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tmp_df = yf_batch_download(intersect_syms, start=ed, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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else:
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tmp_df = yf_batch_download(intersect_syms, start=last_day, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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df = pd.concat([df[:-1], tmp_df])
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# get data online when new stks were added
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new_stks = np.setdiff1d(symbols, exist_syms).tolist()
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if not new_stks == []:
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tmp_df = yf_batch_download(new_stks, start=sd, end=ed, \
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batch_size=BATCHSIZE, time_gap=TIMEGAP)
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if len(new_stks) == 1:
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tuples = list(zip(tmp_df.columns.values.tolist(), \
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[new_stks[0]]*len(tmp_df.columns.values)))
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tmp_df.columns = pd.MultiIndex.from_tuples(tuples, names=[None, None])
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df = df.join(tmp_df)
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# df.to_pickle(DATAFILE) # save to local, overwrite existing file
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# if os.path.exists(REFRESH):
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# with open(REFRESH, 'w'):
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# pass
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df = df.dropna(subset=[('Adj Close', 'SPY')])
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price = df['Adj Close']
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price = fill_missing_data(price)
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volume = df['Volume']
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volume = volume.fillna(0)
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||||||
|
|
||||||
# if len(symbols) == 1:
|
|
||||||
# price.name = symbols[0]
|
|
||||||
# volume.name = symbols[0]
|
|
||||||
# price = price.to_frame()
|
|
||||||
# volume = volume.to_frame()
|
|
||||||
|
|
||||||
if addSPY==False:
|
|
||||||
price = price[org_sym]
|
|
||||||
volume = volume[org_sym]
|
|
||||||
|
|
||||||
return price, volume
|
|
||||||
|
|
||||||
|
|
||||||
# def get_price_volume_online(symbols, dates, addSPY=False):
|
|
||||||
# """
|
|
||||||
# Read stock data (adjusted close and volume) for given symbols from Yahoo
|
|
||||||
# Finance
|
|
||||||
# """
|
|
||||||
# org_sym = symbols
|
|
||||||
# sd = dates[0]
|
|
||||||
# ed = dates[-1]
|
|
||||||
# # if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
|
|
||||||
# if 'SPY' not in symbols:
|
|
||||||
# symbols = ['SPY'] + symbols
|
|
||||||
# df = yf.download(symbols, start=sd, end = ed)
|
|
||||||
# if len(symbols) == 1:
|
|
||||||
# df = df.dropna(subset = ['Adj Close'])
|
|
||||||
# else:
|
|
||||||
# df = df.dropna(subset=[('Adj Close', 'SPY')])
|
|
||||||
# price = df['Adj Close']
|
|
||||||
# price = fill_missing_data(price)
|
|
||||||
# volume = df['Volume']
|
|
||||||
# volume = volume.fillna(0)
|
|
||||||
|
|
||||||
# if len(symbols) == 1:
|
|
||||||
# price.name = symbols[0]
|
|
||||||
# volume.name = symbols[0]
|
|
||||||
# price = price.to_frame()
|
|
||||||
# volume = volume.to_frame()
|
|
||||||
|
|
||||||
# if addSPY==False:
|
|
||||||
# price = price[org_sym]
|
|
||||||
# volume = volume[org_sym]
|
|
||||||
|
|
||||||
# return price, volume
|
|
||||||
|
|
||||||
def get_watchlist(website: str = WEBSITE):
|
|
||||||
page = requests.get(WEBSITE)
|
|
||||||
# page = requests.get(WEBSITE, verify=False) # skip certificate check for https
|
|
||||||
tree = html.fromstring(page.content)
|
|
||||||
watchlist = tree.xpath('//*[@id="dokuwiki__content"]/div[1]/div/div[3]/div/pre/text()')[0]
|
|
||||||
file_name = StringIO(watchlist)
|
|
||||||
df = pd.read_csv(file_name, index_col = 'Symbol',
|
|
||||||
comment = '#', na_filter=False)
|
|
||||||
return df
|
|
||||||
|
|
||||||
# def get_watchlist(file_name: str = 'watchlist.csv'):
|
|
||||||
# df = pd.read_csv(file_name, index_col = 'Symbol',
|
|
||||||
# comment = '#', na_filter=False)
|
|
||||||
# return df
|
|
||||||
|
|
||||||
# def get_data(symbols, dates, addSPY=True, colname = 'Adj Close'):
|
|
||||||
# """
|
|
||||||
# Read stock data (adjusted close) for given symbols from CSV files.
|
|
||||||
|
|
||||||
# (done) TODO: there are nan values in the data when addSPY=False is passed. The
|
|
||||||
# strategy should be using SPY to clean the data first including fill
|
|
||||||
# forward and fill backward, then to drop the SPY if addSPY=False
|
|
||||||
# """
|
|
||||||
# org_sym = symbols
|
|
||||||
# df = pd.DataFrame(index=dates)
|
|
||||||
# # if addSPY and 'SPY' not in symbols: # add SPY for reference, if absent
|
|
||||||
# # symbols = ['SPY'] + symbols
|
|
||||||
# if 'SPY' not in symbols:
|
|
||||||
# symbols = ['SPY'] + symbols
|
|
||||||
# for symbol in symbols:
|
|
||||||
# df_temp = pd.read_csv(symbol_to_path(symbol), index_col='Date',
|
|
||||||
# parse_dates=True, usecols=['Date', colname], na_values=['nan'])
|
|
||||||
# df_temp = df_temp.rename(columns={colname: symbol})
|
|
||||||
# df = df.join(df_temp)
|
|
||||||
# if symbol == 'SPY': # drop dates SPY did not trade
|
|
||||||
# df = df.dropna(subset=["SPY"])
|
|
||||||
# # fill missing data
|
|
||||||
# df = fill_missing_data(df)
|
|
||||||
# if addSPY == False: # drop SPY
|
|
||||||
# # df = df.drop(columns=['SPY'])
|
|
||||||
# df = df[org_sym]
|
|
||||||
|
|
||||||
# return df
|
|
||||||
|
|
||||||
|
|
||||||
def plot_data(df, axs=[], title=[], xlabel='', ylabel=''):
|
|
||||||
|
|
||||||
"""Plot stock prices with a custom title and meaningful axis labels."""
|
|
||||||
if axs == []:
|
|
||||||
ax = df.plot(title = title)
|
|
||||||
else:
|
|
||||||
ax = df.plot(ax=axs, title=title)
|
|
||||||
ax.set_xlabel(xlabel)
|
|
||||||
ax.set_ylabel(ylabel)
|
|
||||||
ax.grid()
|
|
||||||
|
|
||||||
|
|
||||||
# def plot_data(df, title=[], xlabel='', ylabel=''):
|
|
||||||
# import matplotlib.pyplot as plt
|
|
||||||
# """Plot stock prices with a custom title and meaningful axis labels."""
|
|
||||||
# ax = df.plot(title=title, fontsize=12, figsize=(10, 7))
|
|
||||||
# ax.set_xlabel(xlabel)
|
|
||||||
# ax.set_ylabel(ylabel)
|
|
||||||
# plt.grid()
|
|
||||||
# plt.show()
|
|
||||||
|
|
||||||
def get_orders_data_file(basefilename):
|
|
||||||
return open(os.path.join(os.environ.get("ORDERS_DATA_DIR",'orders/'),basefilename))
|
|
||||||
|
|
||||||
def get_learner_data_file(basefilename):
|
|
||||||
return open(os.path.join(os.environ.get("LEARNER_DATA_DIR",'Data/'),basefilename),'r')
|
|
||||||
|
|
||||||
def get_robot_world_file(basefilename):
|
|
||||||
return open(os.path.join(os.environ.get("ROBOT_WORLDS_DIR",'testworlds/'),basefilename))
|
|
||||||
|
|
||||||
|
|
||||||
def test_code():
|
|
||||||
|
|
||||||
symbol = ['GOOG', 'AMZN']
|
|
||||||
# lookback years
|
|
||||||
lb_year = 0.08
|
|
||||||
ed = dt.datetime.today()
|
|
||||||
sd = ed - dt.timedelta(days = 365 * lb_year + 1)
|
|
||||||
# If ed or sd falls on to a non-trading day, you might get warnings saying
|
|
||||||
# "No data found for this date range, symbol may be delisted". This is
|
|
||||||
# normal behavior.
|
|
||||||
prices, volume = get_price_volume(symbol, pd.date_range(sd, ed), addSPY=False)
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == '__main__':
|
|
||||||
test_code()
|
|
||||||
Loading…
x
Reference in New Issue
Block a user