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trending only returns if data was retrieved. #75
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@ -25,7 +25,7 @@ class IEX_Symbol:
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searched_symbols = {}
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otc_list = []
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charts = {}
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trending_cache = ["Trending Stocks Currently Unavailable."]
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trending_cache = None
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def __init__(self) -> None:
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"""Creates a Symbol Object
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@ -22,7 +22,7 @@ class cg_Crypto:
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vs_currency = "usd" # simple/supported_vs_currencies for list of options
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searched_symbols = {}
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trending_cache = ["Trending Coins Currently Unavailable."]
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trending_cache = None
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def __init__(self) -> None:
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"""Creates a Symbol Object
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922
symbol_router.py
922
symbol_router.py
@ -1,458 +1,466 @@
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"""Function that routes symbols to the correct API provider.
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"""
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import datetime
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import random
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import re
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from logging import critical, debug, error, info, warning
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import pandas as pd
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import schedule
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from fuzzywuzzy import fuzz
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from cg_Crypto import cg_Crypto
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from IEX_Symbol import IEX_Symbol
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from Symbol import Coin, Stock, Symbol
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class Router:
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STOCK_REGEX = "(?:^|[^\\$])\\$([a-zA-Z.]{1,6})"
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CRYPTO_REGEX = "[$]{2}([a-zA-Z]{1,20})"
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searched_symbols = {}
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trending_count = {}
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def __init__(self):
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self.stock = IEX_Symbol()
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self.crypto = cg_Crypto()
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"""Function that routes symbols to the correct API provider.
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"""
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import datetime
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import random
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import re
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from logging import critical, debug, error, info, warning
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import pandas as pd
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import schedule
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from fuzzywuzzy import fuzz
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from cg_Crypto import cg_Crypto
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from IEX_Symbol import IEX_Symbol
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from Symbol import Coin, Stock, Symbol
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class Router:
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STOCK_REGEX = "(?:^|[^\\$])\\$([a-zA-Z.]{1,6})"
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CRYPTO_REGEX = "[$]{2}([a-zA-Z]{1,20})"
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searched_symbols = {}
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trending_count = {}
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def __init__(self):
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self.stock = IEX_Symbol()
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self.crypto = cg_Crypto()
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schedule.every().hour.do(self.trending_decay)
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def trending_decay(self, decay=0.5):
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"""Decays the value of each trending stock by a multiplier"""
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info("Decaying trending symbols.")
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if self.trending_count:
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for key in self.trending_count.keys():
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if self.trending_count[key] < 0.01:
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# This just makes sure were not keeping around keys that havent been called in a very long time.
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self.trending_count.pop(key, None)
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else:
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self.trending_count[key] = self.trending_count[key] * decay
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def find_symbols(self, text: str) -> list[Symbol]:
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"""Finds stock tickers starting with a dollar sign, and cryptocurrencies with two dollar signs
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in a blob of text and returns them in a list.
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Parameters
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----------
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text : str
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Blob of text.
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Returns
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-------
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list[Symbol]
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List of stock symbols as Symbol objects
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"""
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schedule.run_pending()
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symbols = []
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stocks = set(re.findall(self.STOCK_REGEX, text))
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for stock in stocks:
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if stock.upper() in self.stock.symbol_list["symbol"].values:
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symbols.append(Stock(stock))
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else:
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info(f"{stock} is not in list of stocks")
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coins = set(re.findall(self.CRYPTO_REGEX, text))
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for coin in coins:
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if coin.lower() in self.crypto.symbol_list["symbol"].values:
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symbols.append(Coin(coin.lower()))
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else:
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info(f"{coin} is not in list of coins")
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if symbols:
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info(symbols)
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for symbol in symbols:
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self.trending_count[symbol.tag] = (
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self.trending_count.get(symbol.tag, 0) + 1
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)
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return symbols
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def status(self, bot_resp) -> str:
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"""Checks for any issues with APIs.
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Returns
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-------
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str
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Human readable text on status of the bot and relevant APIs
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"""
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stats = f"""
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Bot Status:
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{bot_resp}
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Stock Market Data:
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{self.stock.status()}
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Cryptocurrency Data:
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{self.crypto.status()}
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"""
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warning(stats)
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return stats
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def search_symbols(self, search: str) -> list[tuple[str, str]]:
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"""Performs a fuzzy search to find stock symbols closest to a search term.
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Parameters
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----------
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search : str
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String used to search, could be a company name or something close to the companies stock ticker.
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Returns
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-------
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list[tuple[str, str]]
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A list tuples of every stock sorted in order of how well they match.
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Each tuple contains: (Symbol, Issue Name).
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"""
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df = pd.concat([self.stock.symbol_list, self.crypto.symbol_list])
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search = search.lower()
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df["Match"] = df.apply(
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lambda x: fuzz.ratio(search, f"{x['symbol']}".lower()),
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axis=1,
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)
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df.sort_values(by="Match", ascending=False, inplace=True)
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# if df["Match"].head().sum() < 300:
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# df["Match"] = df.apply(
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# lambda x: fuzz.partial_ratio(search, x["name"].lower()),
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# axis=1,
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# )
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# df.sort_values(by="Match", ascending=False, inplace=True)
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symbols = df.head(20)
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symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
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self.searched_symbols[search] = symbol_list
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return symbol_list
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def inline_search(self, search: str) -> list[tuple[str, str]]:
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"""Searches based on the shortest symbol that contains the same string as the search.
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Should be very fast compared to a fuzzy search.
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Parameters
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----------
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search : str
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String used to match against symbols.
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Returns
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-------
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list[tuple[str, str]]
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Each tuple contains: (Symbol, Issue Name).
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"""
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df = pd.concat([self.stock.symbol_list, self.crypto.symbol_list])
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search = search.lower()
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df = df[df["type_id"].str.contains(search, regex=False)].sort_values(
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by="type_id", key=lambda x: x.str.len()
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)
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symbols = df.head(20)
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symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
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self.searched_symbols[search] = symbol_list
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return symbol_list
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def price_reply(self, symbols: list[Symbol]) -> list[str]:
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"""Returns current market price or after hours if its available for a given stock symbol.
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Parameters
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----------
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symbols : list
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List of stock symbols.
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Returns
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-------
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Dict[str, str]
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Each symbol passed in is a key with its value being a human readable
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markdown formatted string of the symbols price and movement.
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"""
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replies = []
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for symbol in symbols:
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info(symbol)
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if isinstance(symbol, Stock):
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replies.append(self.stock.price_reply(symbol))
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elif isinstance(symbol, Coin):
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replies.append(self.crypto.price_reply(symbol))
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else:
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info(f"{symbol} is not a Stock or Coin")
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return replies
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def dividend_reply(self, symbols: list) -> list[str]:
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"""Returns the most recent, or next dividend date for a stock symbol.
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Parameters
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----------
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symbols : list
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List of stock symbols.
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Returns
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-------
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Dict[str, str]
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Each symbol passed in is a key with its value being a human readable
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formatted string of the symbols div dates.
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"""
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replies = []
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for symbol in symbols:
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if isinstance(symbol, Stock):
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replies.append(self.stock.dividend_reply(symbol))
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elif isinstance(symbol, Coin):
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replies.append("Cryptocurrencies do no have Dividends.")
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else:
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debug(f"{symbol} is not a Stock or Coin")
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return replies
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def news_reply(self, symbols: list) -> list[str]:
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"""Gets recent english news on stock symbols.
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Parameters
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----------
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symbols : list
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List of stock symbols.
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Returns
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-------
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Dict[str, str]
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Each symbol passed in is a key with its value being a human
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readable markdown formatted string of the symbols news.
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"""
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replies = []
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for symbol in symbols:
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if isinstance(symbol, Stock):
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replies.append(self.stock.news_reply(symbol))
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elif isinstance(symbol, Coin):
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# replies.append(self.crypto.news_reply(symbol))
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replies.append(
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"News is not yet supported for cryptocurrencies. If you have any suggestions for news sources please contatct @MisterBiggs"
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)
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else:
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debug(f"{symbol} is not a Stock or Coin")
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return replies
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def info_reply(self, symbols: list) -> list[str]:
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"""Gets information on stock symbols.
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Parameters
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----------
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symbols : list[str]
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List of stock symbols.
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Returns
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-------
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Dict[str, str]
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Each symbol passed in is a key with its value being a human readable formatted
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string of the symbols information.
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"""
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replies = []
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for symbol in symbols:
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if isinstance(symbol, Stock):
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replies.append(self.stock.info_reply(symbol))
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elif isinstance(symbol, Coin):
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replies.append(self.crypto.info_reply(symbol))
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else:
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debug(f"{symbol} is not a Stock or Coin")
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return replies
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def intra_reply(self, symbol: Symbol) -> pd.DataFrame:
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"""Returns price data for a symbol since the last market open.
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Parameters
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----------
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symbol : str
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Stock symbol.
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Returns
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-------
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pd.DataFrame
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Returns a timeseries dataframe with high, low, and volume data if its available.
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Otherwise returns empty pd.DataFrame.
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"""
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if isinstance(symbol, Stock):
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return self.stock.intra_reply(symbol)
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elif isinstance(symbol, Coin):
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return self.crypto.intra_reply(symbol)
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else:
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debug(f"{symbol} is not a Stock or Coin")
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return pd.DataFrame()
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def chart_reply(self, symbol: Symbol) -> pd.DataFrame:
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"""Returns price data for a symbol of the past month up until the previous trading days close.
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Also caches multiple requests made in the same day.
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Parameters
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----------
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symbol : str
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Stock symbol.
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Returns
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-------
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pd.DataFrame
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Returns a timeseries dataframe with high, low, and volume data if its available.
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Otherwise returns empty pd.DataFrame.
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"""
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if isinstance(symbol, Stock):
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return self.stock.chart_reply(symbol)
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elif isinstance(symbol, Coin):
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return self.crypto.chart_reply(symbol)
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else:
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debug(f"{symbol} is not a Stock or Coin")
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return pd.DataFrame()
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def stat_reply(self, symbols: list[Symbol]) -> list[str]:
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"""Gets key statistics for each symbol in the list
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Parameters
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----------
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symbols : list[str]
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List of stock symbols
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Returns
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-------
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Dict[str, str]
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Each symbol passed in is a key with its value being a human readable
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formatted string of the symbols statistics.
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"""
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replies = []
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for symbol in symbols:
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if isinstance(symbol, Stock):
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replies.append(self.stock.stat_reply(symbol))
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elif isinstance(symbol, Coin):
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replies.append(self.crypto.stat_reply(symbol))
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else:
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debug(f"{symbol} is not a Stock or Coin")
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return replies
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def cap_reply(self, symbols: list[Symbol]) -> list[str]:
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"""Gets market cap for each symbol in the list
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Parameters
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----------
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symbols : list[str]
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List of stock symbols
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Returns
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-------
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Dict[str, str]
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Each symbol passed in is a key with its value being a human readable
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formatted string of the symbols market cap.
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"""
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replies = []
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for symbol in symbols:
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if isinstance(symbol, Stock):
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replies.append(self.stock.cap_reply(symbol))
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elif isinstance(symbol, Coin):
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replies.append(self.crypto.cap_reply(symbol))
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else:
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debug(f"{symbol} is not a Stock or Coin")
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return replies
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def trending(self) -> str:
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"""Checks APIs for trending symbols.
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Returns
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-------
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list[str]
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List of preformatted strings to be sent to user.
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"""
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stocks = self.stock.trending()
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coins = self.crypto.trending()
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reply = ""
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reply += "Trending on the Stock Bot:\n"
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reply += "-" * len("Trending on the Stock Bot:") + "\n"
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sorted_trending = [
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s[0] for s in sorted(self.trending_count.items(), key=lambda item: item[1])
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][::-1][0:5]
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for t in sorted_trending:
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reply += self.price_reply(self.find_symbols(t))[0] + "\n"
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reply += "\n\nTrending Stocks:\n"
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reply += "-" * len("Trending Stocks:") + "\n"
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for stock in stocks:
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reply += stock + "\n"
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reply += "\n\nTrending Crypto:\n"
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reply += "-" * len("Trending Crypto:") + "\n"
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for coin in coins:
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reply += coin + "\n"
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return reply
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def random_pick(self) -> str:
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choice = random.choice(
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list(self.stock.symbol_list["description"])
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+ list(self.crypto.symbol_list["description"])
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)
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hold = (
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datetime.date.today() + datetime.timedelta(random.randint(1, 365))
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).strftime("%b %d, %Y")
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return f"{choice}\nBuy and hold until: {hold}"
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def batch_price_reply(self, symbols: list[Symbol]) -> list[str]:
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"""Returns current market price or after hours if its available for a given stock symbol.
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Parameters
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----------
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symbols : list
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List of stock symbols.
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Returns
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-------
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Dict[str, str]
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Each symbol passed in is a key with its value being a human readable
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markdown formatted string of the symbols price and movement.
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"""
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replies = []
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stocks = []
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coins = []
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for symbol in symbols:
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if isinstance(symbol, Stock):
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stocks.append(symbol)
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elif isinstance(symbol, Coin):
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coins.append(symbol)
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else:
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debug(f"{symbol} is not a Stock or Coin")
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if stocks:
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# IEX batch endpoint doesnt seem to be working right now
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for stock in stocks:
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replies.append(self.stock.price_reply(stock))
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if coins:
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replies = replies + self.crypto.batch_price(coins)
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return replies
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def trending_decay(self, decay=0.5):
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"""Decays the value of each trending stock by a multiplier"""
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info("Decaying trending symbols.")
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if self.trending_count:
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for key in self.trending_count.keys():
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if self.trending_count[key] < 0.01:
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# This just makes sure were not keeping around keys that havent been called in a very long time.
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self.trending_count.pop(key, None)
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else:
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self.trending_count[key] = self.trending_count[key] * decay
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def find_symbols(self, text: str) -> list[Symbol]:
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"""Finds stock tickers starting with a dollar sign, and cryptocurrencies with two dollar signs
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in a blob of text and returns them in a list.
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Parameters
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----------
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text : str
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Blob of text.
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Returns
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-------
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list[Symbol]
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List of stock symbols as Symbol objects
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"""
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schedule.run_pending()
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symbols = []
|
||||
stocks = set(re.findall(self.STOCK_REGEX, text))
|
||||
for stock in stocks:
|
||||
if stock.upper() in self.stock.symbol_list["symbol"].values:
|
||||
symbols.append(Stock(stock))
|
||||
else:
|
||||
info(f"{stock} is not in list of stocks")
|
||||
|
||||
coins = set(re.findall(self.CRYPTO_REGEX, text))
|
||||
for coin in coins:
|
||||
if coin.lower() in self.crypto.symbol_list["symbol"].values:
|
||||
symbols.append(Coin(coin.lower()))
|
||||
else:
|
||||
info(f"{coin} is not in list of coins")
|
||||
|
||||
if symbols:
|
||||
info(symbols)
|
||||
for symbol in symbols:
|
||||
self.trending_count[symbol.tag] = (
|
||||
self.trending_count.get(symbol.tag, 0) + 1
|
||||
)
|
||||
|
||||
return symbols
|
||||
|
||||
def status(self, bot_resp) -> str:
|
||||
"""Checks for any issues with APIs.
|
||||
|
||||
Returns
|
||||
-------
|
||||
str
|
||||
Human readable text on status of the bot and relevant APIs
|
||||
"""
|
||||
|
||||
stats = f"""
|
||||
Bot Status:
|
||||
{bot_resp}
|
||||
|
||||
Stock Market Data:
|
||||
{self.stock.status()}
|
||||
|
||||
Cryptocurrency Data:
|
||||
{self.crypto.status()}
|
||||
"""
|
||||
|
||||
warning(stats)
|
||||
|
||||
return stats
|
||||
|
||||
def search_symbols(self, search: str) -> list[tuple[str, str]]:
|
||||
"""Performs a fuzzy search to find stock symbols closest to a search term.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
search : str
|
||||
String used to search, could be a company name or something close to the companies stock ticker.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list[tuple[str, str]]
|
||||
A list tuples of every stock sorted in order of how well they match.
|
||||
Each tuple contains: (Symbol, Issue Name).
|
||||
"""
|
||||
|
||||
df = pd.concat([self.stock.symbol_list, self.crypto.symbol_list])
|
||||
|
||||
search = search.lower()
|
||||
|
||||
df["Match"] = df.apply(
|
||||
lambda x: fuzz.ratio(search, f"{x['symbol']}".lower()),
|
||||
axis=1,
|
||||
)
|
||||
|
||||
df.sort_values(by="Match", ascending=False, inplace=True)
|
||||
# if df["Match"].head().sum() < 300:
|
||||
# df["Match"] = df.apply(
|
||||
# lambda x: fuzz.partial_ratio(search, x["name"].lower()),
|
||||
# axis=1,
|
||||
# )
|
||||
|
||||
# df.sort_values(by="Match", ascending=False, inplace=True)
|
||||
|
||||
symbols = df.head(20)
|
||||
symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
|
||||
self.searched_symbols[search] = symbol_list
|
||||
return symbol_list
|
||||
|
||||
def inline_search(self, search: str) -> list[tuple[str, str]]:
|
||||
"""Searches based on the shortest symbol that contains the same string as the search.
|
||||
Should be very fast compared to a fuzzy search.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
search : str
|
||||
String used to match against symbols.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list[tuple[str, str]]
|
||||
Each tuple contains: (Symbol, Issue Name).
|
||||
"""
|
||||
|
||||
df = pd.concat([self.stock.symbol_list, self.crypto.symbol_list])
|
||||
|
||||
search = search.lower()
|
||||
|
||||
df = df[df["type_id"].str.contains(search, regex=False)].sort_values(
|
||||
by="type_id", key=lambda x: x.str.len()
|
||||
)
|
||||
|
||||
symbols = df.head(20)
|
||||
symbol_list = list(zip(list(symbols["symbol"]), list(symbols["description"])))
|
||||
self.searched_symbols[search] = symbol_list
|
||||
return symbol_list
|
||||
|
||||
def price_reply(self, symbols: list[Symbol]) -> list[str]:
|
||||
"""Returns current market price or after hours if its available for a given stock symbol.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbols : list
|
||||
List of stock symbols.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, str]
|
||||
Each symbol passed in is a key with its value being a human readable
|
||||
markdown formatted string of the symbols price and movement.
|
||||
"""
|
||||
replies = []
|
||||
|
||||
for symbol in symbols:
|
||||
info(symbol)
|
||||
if isinstance(symbol, Stock):
|
||||
replies.append(self.stock.price_reply(symbol))
|
||||
elif isinstance(symbol, Coin):
|
||||
replies.append(self.crypto.price_reply(symbol))
|
||||
else:
|
||||
info(f"{symbol} is not a Stock or Coin")
|
||||
|
||||
return replies
|
||||
|
||||
def dividend_reply(self, symbols: list) -> list[str]:
|
||||
"""Returns the most recent, or next dividend date for a stock symbol.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbols : list
|
||||
List of stock symbols.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, str]
|
||||
Each symbol passed in is a key with its value being a human readable
|
||||
formatted string of the symbols div dates.
|
||||
"""
|
||||
replies = []
|
||||
for symbol in symbols:
|
||||
if isinstance(symbol, Stock):
|
||||
replies.append(self.stock.dividend_reply(symbol))
|
||||
elif isinstance(symbol, Coin):
|
||||
replies.append("Cryptocurrencies do no have Dividends.")
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
|
||||
return replies
|
||||
|
||||
def news_reply(self, symbols: list) -> list[str]:
|
||||
"""Gets recent english news on stock symbols.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbols : list
|
||||
List of stock symbols.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, str]
|
||||
Each symbol passed in is a key with its value being a human
|
||||
readable markdown formatted string of the symbols news.
|
||||
"""
|
||||
replies = []
|
||||
|
||||
for symbol in symbols:
|
||||
if isinstance(symbol, Stock):
|
||||
replies.append(self.stock.news_reply(symbol))
|
||||
elif isinstance(symbol, Coin):
|
||||
# replies.append(self.crypto.news_reply(symbol))
|
||||
replies.append(
|
||||
"News is not yet supported for cryptocurrencies. If you have any suggestions for news sources please contatct @MisterBiggs"
|
||||
)
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
|
||||
return replies
|
||||
|
||||
def info_reply(self, symbols: list) -> list[str]:
|
||||
"""Gets information on stock symbols.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbols : list[str]
|
||||
List of stock symbols.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, str]
|
||||
Each symbol passed in is a key with its value being a human readable formatted
|
||||
string of the symbols information.
|
||||
"""
|
||||
replies = []
|
||||
|
||||
for symbol in symbols:
|
||||
if isinstance(symbol, Stock):
|
||||
replies.append(self.stock.info_reply(symbol))
|
||||
elif isinstance(symbol, Coin):
|
||||
replies.append(self.crypto.info_reply(symbol))
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
|
||||
return replies
|
||||
|
||||
def intra_reply(self, symbol: Symbol) -> pd.DataFrame:
|
||||
"""Returns price data for a symbol since the last market open.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbol : str
|
||||
Stock symbol.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
Returns a timeseries dataframe with high, low, and volume data if its available.
|
||||
Otherwise returns empty pd.DataFrame.
|
||||
"""
|
||||
|
||||
if isinstance(symbol, Stock):
|
||||
return self.stock.intra_reply(symbol)
|
||||
elif isinstance(symbol, Coin):
|
||||
return self.crypto.intra_reply(symbol)
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
return pd.DataFrame()
|
||||
|
||||
def chart_reply(self, symbol: Symbol) -> pd.DataFrame:
|
||||
"""Returns price data for a symbol of the past month up until the previous trading days close.
|
||||
Also caches multiple requests made in the same day.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbol : str
|
||||
Stock symbol.
|
||||
|
||||
Returns
|
||||
-------
|
||||
pd.DataFrame
|
||||
Returns a timeseries dataframe with high, low, and volume data if its available.
|
||||
Otherwise returns empty pd.DataFrame.
|
||||
"""
|
||||
if isinstance(symbol, Stock):
|
||||
return self.stock.chart_reply(symbol)
|
||||
elif isinstance(symbol, Coin):
|
||||
return self.crypto.chart_reply(symbol)
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
return pd.DataFrame()
|
||||
|
||||
def stat_reply(self, symbols: list[Symbol]) -> list[str]:
|
||||
"""Gets key statistics for each symbol in the list
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbols : list[str]
|
||||
List of stock symbols
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, str]
|
||||
Each symbol passed in is a key with its value being a human readable
|
||||
formatted string of the symbols statistics.
|
||||
"""
|
||||
replies = []
|
||||
|
||||
for symbol in symbols:
|
||||
if isinstance(symbol, Stock):
|
||||
replies.append(self.stock.stat_reply(symbol))
|
||||
elif isinstance(symbol, Coin):
|
||||
replies.append(self.crypto.stat_reply(symbol))
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
|
||||
return replies
|
||||
|
||||
def cap_reply(self, symbols: list[Symbol]) -> list[str]:
|
||||
"""Gets market cap for each symbol in the list
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbols : list[str]
|
||||
List of stock symbols
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, str]
|
||||
Each symbol passed in is a key with its value being a human readable
|
||||
formatted string of the symbols market cap.
|
||||
"""
|
||||
replies = []
|
||||
|
||||
for symbol in symbols:
|
||||
if isinstance(symbol, Stock):
|
||||
replies.append(self.stock.cap_reply(symbol))
|
||||
elif isinstance(symbol, Coin):
|
||||
replies.append(self.crypto.cap_reply(symbol))
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
|
||||
return replies
|
||||
|
||||
def trending(self) -> str:
|
||||
"""Checks APIs for trending symbols.
|
||||
|
||||
Returns
|
||||
-------
|
||||
list[str]
|
||||
List of preformatted strings to be sent to user.
|
||||
"""
|
||||
|
||||
stocks = self.stock.trending()
|
||||
coins = self.crypto.trending()
|
||||
|
||||
reply = ""
|
||||
|
||||
if self.trending_count:
|
||||
reply += "🔥Trending on the Stock Bot:\n`"
|
||||
reply += "━" * len("Trending on the Stock Bot:") + "`\n"
|
||||
|
||||
sorted_trending = [
|
||||
s[0]
|
||||
for s in sorted(self.trending_count.items(), key=lambda item: item[1])
|
||||
][::-1][0:5]
|
||||
|
||||
for t in sorted_trending:
|
||||
reply += self.price_reply(self.find_symbols(t))[0] + "\n"
|
||||
|
||||
if stocks:
|
||||
reply += "\n\n💵Trending Stocks:\n`"
|
||||
reply += "━" * len("Trending Stocks:") + "`\n"
|
||||
for stock in stocks:
|
||||
reply += stock + "\n"
|
||||
|
||||
if coins:
|
||||
reply += "\n\n🦎Trending Crypto:\n`"
|
||||
reply += "━" * len("Trending Crypto:") + "`\n"
|
||||
for coin in coins:
|
||||
reply += coin + "\n"
|
||||
|
||||
if reply:
|
||||
return reply
|
||||
else:
|
||||
warning("Failed to collect trending data.")
|
||||
return "Trending data is not currently available."
|
||||
|
||||
def random_pick(self) -> str:
|
||||
|
||||
choice = random.choice(
|
||||
list(self.stock.symbol_list["description"])
|
||||
+ list(self.crypto.symbol_list["description"])
|
||||
)
|
||||
hold = (
|
||||
datetime.date.today() + datetime.timedelta(random.randint(1, 365))
|
||||
).strftime("%b %d, %Y")
|
||||
|
||||
return f"{choice}\nBuy and hold until: {hold}"
|
||||
|
||||
def batch_price_reply(self, symbols: list[Symbol]) -> list[str]:
|
||||
"""Returns current market price or after hours if its available for a given stock symbol.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
symbols : list
|
||||
List of stock symbols.
|
||||
|
||||
Returns
|
||||
-------
|
||||
Dict[str, str]
|
||||
Each symbol passed in is a key with its value being a human readable
|
||||
markdown formatted string of the symbols price and movement.
|
||||
"""
|
||||
replies = []
|
||||
stocks = []
|
||||
coins = []
|
||||
|
||||
for symbol in symbols:
|
||||
if isinstance(symbol, Stock):
|
||||
stocks.append(symbol)
|
||||
elif isinstance(symbol, Coin):
|
||||
coins.append(symbol)
|
||||
else:
|
||||
debug(f"{symbol} is not a Stock or Coin")
|
||||
|
||||
if stocks:
|
||||
# IEX batch endpoint doesnt seem to be working right now
|
||||
for stock in stocks:
|
||||
replies.append(self.stock.price_reply(stock))
|
||||
if coins:
|
||||
replies = replies + self.crypto.batch_price(coins)
|
||||
|
||||
return replies
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user