Robinhood API

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PIPELINE, stderr= subprocess. PIPELINE) out, err = p.communicate() if len( out) < 1: print ‘Error connecting to server:’ print err else: print ‘Connection successful.’ return out def logout( token): command=’curl -v -H “Accept: application/json” -H “Permission: Token ‘+ token+'” -d “”‘ p = subprocess. Popen( command, shell= True, stdout= subprocess. PIPELINE, stderr= subprocess. PIPELINE) out, err = p.communicate() def account Information( token): command=’curl -v -H “Accept: application/json” -H “Permission: Token ‘+ token+'”‘ p = subprocess.

PIPELINE, stderr= subprocess. PIPE) out, err = p.communicate() return out def primary(): print ‘Logging in …’ token = login(‘ ENTER USERNAME HERE’, ‘GET IN PASSWORD HERE’) token = token [10: -2] print ‘token: ‘, token print ‘Grabbing account information …’ account_info = account Details( token) print account_info print ‘Logging out …’ logout( token) main() All this code does is get some info about XIV from Robinhood itself.

So I simply search through it and discover the data I desire, transform it to a float and designate it to a variable. import subprocess class information(): def __ init __( self, stock): self.stock = stock parameter_list = [‘ open’, ‘high’, ‘low’, ‘volume’, ‘average_volume’, ‘last_trade_price’, ‘previous_close’] command_list = [‘ curl -v -H “Accept: application/json”‘, ‘curl -v -H “Accept: application/json”‘] for commands in command_list: command = commands p = subprocess.

PIPE, stderr= subprocess. PIPELINE) out, err = p.communicate() string = out for p in parameter_list: specification = p if specification in string: x = 0 output=”iteration = 0 for i in string: if i!= criterion [x]: x = 0 if i == criterion [x]: x = x +1 if x == len( parameter): eow Position = model break iteration = model + 1 target_position = eow Position + 4 for i in string [target_position:]: if i == ‘”‘: break elif i == ‘u’: output=’NULL’ break else: output = output+ i if output!= ‘NULL’: output = float( output) if p == ‘open’: self.open = output if p == ‘high’: self.high = output if p == ‘low’: self.low = output if p == ‘volume’: self.volume = output if p == ‘average_volume’: self.average _ volume = output if p == ‘last_trade_price’: self.current = output if p == ‘previous_close’: self.close = output XIV = information(‘ XIV’) print ‘Current cost: ‘, XIV.current print ‘Today \’s Open: ‘, XIV.open print ‘Today \’s High: ‘, XIV.high print ‘Today \’s Low: ‘, XIV.low print ‘Today \’s Volume: ‘, XIV.volume print ‘Average Volume: ‘, XIV.average _ volume This downloads trade information for SPY from google finance, then finds what the very best sma window would be.

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Note if pyalgotrade functions, such as sma(), discovers bad information in the information downloaded from google financing it will fault the script. You’ll have to open the csv. file by hand and go into in the information. import os import datetime import pyalgotrade.logger from pyalgotrade import bar from pyalgotrade.barfeed import googlefeed from pyalgotrade.utils import csvutils from pyalgotrade import strategy from pyalgotrade.technical import ma class My Method( technique.

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SMA( feed [instrument] get Price Data Series(), sma Period) def on Enter Okay( self, position): exec Details = position.get Entry Order(). get Execution Details() self.info(” BUY at $%.2 f” % (officer Info.get Cost())) def on Enter Canceled( self, position): self. __ position = None def on Exit Ok( self, position): officer Info = position.get Exit Order(). get Execution Info() self.info(” OFFER at $%.2 f” % (officer Info.get Cost())) self. __ position = None def on Exit Canceled( self, position): # If the exit was canceled, re-submit it.

if self. __ sma [-1] is None: return bar = bars [self. __ instrument] # If a position was not opened, examine if we should enter a long position. if self. __ position is None: if bar.get Price() > self. __ sma [-1]: # Go into a buy market order for 10 shares. The order is great till canceled. self. __ position = self.enter Long( self. __ instrument, 10, Real) # Examine if we have to exit the position.

Feed() feed.add Bars From CSV(” spy”, “spy_2016. csv”) # Evaluate the method with the feed. my Technique = My Strategy( feed, “spy”, sma Duration) my Strategy.run() print “Last portfolio value: $%.2 f” % my Strategy.get Broker(). get Equity() return my Strategy.get Broker(). get Equity() def download_csv( instrument, start, end): url =” params = “q”: instrument, “startdate”: begin.strftime(“% Y-% m-% d”), “enddate”: end.strftime(“% Y-% m-% d”), “output”: “csv”, return csvutils.download _ csv( url, url_params= params, content_type=” application/vnd. ms-excel”) def download_daily_bars( instrument, start, end, csv File): “”” Download day-to-day bars from Google Finance for a given year.

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: type instrument: string.: param year: The year.: type year: int.: param csv File: The path to the CSV file to write.: type csv File: string. “”” bars = download_csv( instrument, datetime.date( start, 1, 1), datetime.date( end, 12, 31)) f = open( csv File, “w”) f.write( bars) f.close() start = 2016 end = 2016 download_daily_bars(‘ spy’, begin, end, ‘spy_2016. csv’) best_performance = 0 for i in range (10, 50): efficiency = run_strategy( i) if performance > best_performance: best_performance = efficiency sma_period = i print “Best performance was sma period:”, sma_period,” @ $%.2 f” %best_performance.

Stock trading app API deals with the banking, security and regulatory intricacy, allowing other start-ups to rapidly develop brokerage apps on top for totally free. It has actually currently crossed $1 billion in deals within a year of launch. The prospective to power the backend of a brand-new generation of fintech apps has actually drawn in a $6 million Series A round for Alpaca led by.

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” I want to make sure that people even outside the U.S. have access” to a method of structure wealth that’s traditionally only “offered to rich individuals” Alpaca co-founder and CEO Yoshi Yokokawa tells me. Alpaca co-founder and CEO Yoshi Yokokawa Hailing from Japan, Yokokawa followed his pals into the financial investment banking market, where he operated at Lehman Brothers up until its collapse.

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Yokokawa ended up very first constructing a business selling deep knowing AI to banks and trading firms in the forex market. Enjoying customers battle to quickly incorporate brand-new innovation revealed the lack of available designer tools. By 2017, he was rotating business and obtaining FINRA approval. Alpaca launched in late 2018, letting developers paste in code to let their users purchase and offer securities.

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