def download_stock_data(index: str, symbols: list, count: int, resolution="D"):
client = Finnhub.Client(api_key=API_KEY)
for symbol in symbols:
stock_candle = client.stock_candle(symbol=symbol, resolution=resolution, count=count)
if stock_candle["s"] != "ok":
continue
directory = os.path.join(os.curdir, "data", index)
if not os.path.exists(directory):
os.mkdir(directory)
file_path = os.path.join(directory, "{}.csv".format(symbol))
with open(file_path, "w", newline='') as fobj:
field_names = [key for key in stock_candle.keys() if key != "s"]
writer = csv.DictWriter(fobj, fieldnames=field_names)
writer.writeheader()
for i in range(len(stock_candle[field_names[0]])):
obj = {key: stock_candle[key][i] for key in field_names}
writer.writerow(obj)
time.sleep(1)
def finnhub_price(share_symbol):
from finnhub import client as Finnhub
client = Finnhub.Client(api_key=config['finnhub_api_key'])
price = client.quote(symbol = share_symbol)
##print("api_call")
return price
from finnhub import client as Finnhub client = Finnhub.Client(api_key="********************") # Get real-time update on the number of COVID-19 (Coronavirus) cases in the US client.covid() # Get general information of a company client.company_profile(symbol="NFLX") # Get latest company's CEO compensation client.ceo_compensation(symbol="NFLX") # Get latest analyst recommendation trends client.recommendation(symbol="NFLX") # Get latest price target consensus client.price_target(symbol="NFLX") # Get latest stock upgrade and downgrade client.upgrade_downgrade(symbol="NFLX") # Get company option chain client.option_chain(symbol="NFLX") # Get company peers client.peers(symbol="NFLX") # Get company quarterly earnings client.earnings(symbol="NFLX")
# https://github.com/s0h3ck/finnhub-api-python-client
from finnhub import client as Finnhub
import pandas as pd
API_key = ''
client = Finnhub.Client(api_key=API_key)
AAPL_candle = client.stock_candle(symbol="AAPL", resolution="D", count=3650)
status = AAPL_candle.pop('s')
if status == 'ok':
AAPL_df = pd.DataFrame.from_dict(AAPL_candle)
# Forex and crpyto has limit 500
# Get general information of a company
client.company_profile(symbol="NFLX")
# Get latest company's CEO compensation
client.ceo_compensation(symbol="NFLX")
# Get latest analyst recommendation trends
client.recommendation(symbol="NFLX")
# Get latest price target consensus
client.price_target(symbol="NFLX")
# Get latest stock upgrade and downgrade
client.upgrade_downgrade(symbol="NFLX")
# Get company option chain
import discord
import asyncio
import os
import time
import datetime
from finnhub import client as Finnhub # api docs: https://finnhub.io/docs/api
import requests
FINNHUB_API_TOKEN = os.environ.get('FINNHUB_API_TOKEN')
FINNHUB_RS_API_TOKEN = os.environ.get('FINNHUB_API_TOKEN_4')
finnhub_client = Finnhub.Client(api_key=FINNHUB_API_TOKEN)
finnhub_rs_client = Finnhub.Client(api_key=FINNHUB_RS_API_TOKEN)
async def rs(ctx, ticker):
"""Sends the stock_price_today() embedded
message and updates it every minute for 5
minutes.
Parameters
----------
ctx : discord.ext.commands.Context
context of the command
ticker : string
stock ticker (ex. AAPL, MSFT, TSLA, ^DJI, BTCUSDT)
"""
status, embed = await stock_price_today(ctx, ticker, True)
message = await ctx.send(embed=embed)
if status != 'ok': # if not okay then don't edit
return
from finnhub import client as Finnhub
import pandas as pd
import datetime
client = Finnhub.Client(api_key="burenjf48v6u5d1lc1cg") #public key
normalize_flag = True
divide_by_max_flag = False
start = int(datetime.datetime(2017, 12, 1).timestamp())
end = int(datetime.datetime(2020, 12, 31).timestamp())
resolution = "W" # 1, 5, 15, 30, 60, D, W, M
value_type = "h" # c v h l
def normalize(numbers):
maximum = max(numbers)
minimum = min(numbers)
return [(x - minimum) / (maximum - minimum) for x in numbers]
def divide_by_max(numbers):
maximum = max(numbers)
return [x / maximum for x in numbers]
def plot_finnhub_data(symbol, finnhub_data, value_type):
if normalize_flag:
finnhub_data[value_type] = normalize(finnhub_data[value_type])
if divide_by_max_flag:
finnhub_data[value_type] = divide_by_max(finnhub_data[value_type])
hub.sentiment('TSLA')
x = hub.economic_code().name.unique()
hub.economic_calendar().to_csv('FH_Econ_Caledanr.csv')
##########################################################################################
from finnhub import client as Finnhub
client = Finnhub.Client(api_key=token)
pd.DataFrame(client.exchange()).to_csv("exchanges.csv") # List supported stock exchanges
# List supported forex symbols for every supported forex exchanges
exchg = pd.read_csv("..\\Tkrs_metadata\\exchanges.csv") # file was downloaded earlier with client.exchange()
df = pd.concat((json_normalize(client.stock_symbol(exchange=exch)).assign(source = exch)
for exch in exchg.FinnHub_code[exchg.Main==1].tolist()), ignore_index = True)
client.company_profile(symbol="NFLX") # PREMIUM: Get general information of a company
json_normalize(client.company_profile_base(symbol="NFLX")) # Free: Get general information of a company
import discord
import os
import time
import re
import datetime
from PIL import Image, ImageFont, ImageDraw
from finnhub import client as Finnhub # api docs: https://finnhub.io/docs/api
import requests
import matplotlib
import mplfinance
import stocks
import pandas as pd
FINNHUB_CHART_API_TOKEN_2 = os.environ.get('FINNHUB_API_TOKEN_2')
FINNHUB_CRYPTO_API_TOKEN_3 = os.environ.get('FINNHUB_API_TOKEN_3')
finnhub_chart_client = Finnhub.Client(api_key=FINNHUB_CHART_API_TOKEN_2)
finnhub_other_crypto_client = Finnhub.Client(
api_key=FINNHUB_CRYPTO_API_TOKEN_3)
async def chart(ctx, ticker, timeframe, chart_type):
"""Called from stock_candle() and stock_line() in bot.py
Creates the stock chart for the specified ticker and timeframe
Parameters
----------
ctx : discord.ext.commands.Context
context of the command
ticker : string
stock ticker (ex. AAPL, MSFT, TSLA, ^DJI, BTCUSDT)
timeframe : string
from finnhub import client as Finnhub
from os import environ
client = Finnhub.Client(api_key=environ['FH_API_KEY'])
def get_candles(symbol, resolution='D', count=7):
response = client.stock_candle(symbol=symbol,
resolution=resolution,
count=count)
return response
#from yahoo_fin import stock_info as si
#si.get_live_price("appl")
#API Key P6Qq2bpJ8nZgCkjyoRq9
# import quandl
# quandl.ApiConfig.api_key = "P6Qq2bpJ8nZgCkjyoRq9"
# data = quandl.get("FRED/GDP", start_date="2001-12-31", end_date="2005-12-31")
from finnhub import client as Finnhub
import pprint
#Imports as dictionary
client = Finnhub.Client(api_key="bpv6h7vrh5rabkt31o30")
#TO_Stocks = client.stock_symbol(exchange="TO")
#pprint.pprint(client.company_profile(symbol="SRT-UN.TO"))
#print("PRICE")
print(client.price_target(symbol="SRT-UN.TO"))
result_df = pd.concat([info_df,FIP_df,CR_df], axis=1, join ="inner")
result_df
"""# Print To Excel"""
#Exports to Local Data. Need to download to computer for now
result_df.to_excel('FIP_FEBRUARY.xlsx',index=True)
"""# Testing RSI api's"""
!pip install finnhub-python
from finnhub import client as Finnhub
from finnhub import client as Finnhub
client = Finnhub.Client(api_key="bsu3fpv48v6r5qhbp2ug")
symbol = "BLNK"
temp = client.aggregate_indicator(symbol, resolution='D')
print(temp)
# temp['technicalAnalysis']['signal']
temp['trend']['adx']
temp['trend']['trending']
# pattern recognition analysis
temp = client.pattern_recognition(symbol,resolution='D')
temp