A HFT Bot built using Alpaca API. Trading strategy implemented in this project:
- Calculate rate of change (ROC) of ask_price of all stocks for last 1 min timeframe from a list (list contains tickers of all stocks you want to watch out for).
- For the stock with highest ROC (let's call it S_1), compare ask_price and last_traded_price (LTP) for 1 minute timeframe (if an order is not yet placed using this bot, timeframe for the 1st trade ever will be 30 mins, then 1 min for every trade after 1st trade is placed).
- If, for S_1, ask_price > LTP, BUY S_1 with 100% capital allocation. Else compare ASK and LTP for stock with 2nd highest ROC (S_2), and repeat step 3 till we find a stock with ASK > LTP in the ROC sorted list.
- Sell after 2% gain.
- Repeat steps 1-4.
- Getting started with Alpaca
Create an account with Alpaca: You can either sign up for a live account or a paper trading account to get started. Navigate to the Alpaca home page – https://alpaca.markets/ and click on Sign up.
Get an API Key: After creating an account, log in to view your API key and secret key. The endpoint used to make calls to the REST API should also be displayed. Take note of all three of these values and save in auth.txt file as key-value pairs.
Install the Alpaca Python Library: Alpaca has a library, otherwise known as the client SDK, which simplifies connecting to the API. To install it, type in pip3 install alpaca-trade-api from your command prompt.
- Connect to Alpaca API using your API Key
key = json.loads(open('auth.txt', 'r').read())
api = alpaca.REST(key['APCA-API-KEY-ID'], key['APCA-API-SECRET-KEY'], base_url='https://paper-api.alpaca.markets', api_version = 'v2')
- Get all stock tickers to monitor from the Tickers.txt file
tickers = open('Tickers.txt', 'r').read()
tickers = tickers.upper().split()
- Get data for all tickers in our list using Alpaca API and save as .csv files for checking our criterias
def get_minute_data(tickers):
def save_min_data(ticker):
prices = api.get_trades(str(ticker), start = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=2)).isoformat(),
end = ((dt.now().astimezone(timezone('America/New_York')))).isoformat(),
limit = 10000).df[['price']]
prices.index = pd.to_datetime(prices.index, format = '%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
prices = prices[~prices.index.duplicated(keep='last')]
quotes = api.get_quotes(str(ticker), start = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=2)).isoformat(),
end = ((dt.now().astimezone(timezone('America/New_York')))).isoformat(),
limit = 10000).df[['ask_price']]
quotes.index = pd.to_datetime(quotes.index, format = '%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
quotes = quotes[~quotes.index.duplicated(keep='last')]
df = pd.merge(prices, quotes, how= 'inner', left_index=True, right_index= True)
df.to_csv('{}.csv'.format(ticker))
for ticker in tickers:
save_min_data(ticker)
NOTE: This function will run periodically to fetch real-time market data for checking criterias.
*NOTE 2: If bot hasn't placed any trade yet (i.e trading for the first time), it will run a different function (since, for the first time, we won't calculate ROC and compare ASK vs LTP for 1-minute timeframe, but for 30 mins)
This function will run to fetch data for the first time
def get_past30_data(tickers):
def save_30_data(ticker):
prices_1 = api.get_trades(str(ticker), start = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=30)).isoformat(),
end = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=28)).isoformat(),
limit = 10000).df[['price']]
prices_2 = api.get_trades(str(ticker), start = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=2)).isoformat(),
end = ((dt.now().astimezone(timezone('America/New_York')))).isoformat(),
limit = 10000).df[['price']]
prices_1.index = pd.to_datetime(prices_1.index, format = '%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
prices_2.index = pd.to_datetime(prices_2.index, format = '%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
prices = pd.concat([prices_1, prices_2])
prices = prices[~prices.index.duplicated(keep='last')]
quotes_1 = api.get_quotes(str(ticker), start = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=30)).isoformat(),
end = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=28)).isoformat(),
limit = 10000).df[['ask_price']]
quotes_2 = api.get_quotes(str(ticker), start = ((dt.now().astimezone(timezone('America/New_York'))) - timedelta(minutes=2)).isoformat(),
end = ((dt.now().astimezone(timezone('America/New_York')))).isoformat(),
limit = 10000).df[['ask_price']]
quotes_1.index = pd.to_datetime(quotes_1.index, format = '%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
quotes_2.index = pd.to_datetime(quotes_2.index, format = '%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
quotes = pd.concat([quotes_1, quotes_2])
quotes = quotes[~quotes.index.duplicated(keep='last')]
df = pd.merge(prices, quotes, how= 'inner', left_index=True, right_index= True)
df.to_csv('{}.csv'.format(ticker))
for ticker in tickers:
save_30_data(ticker)
- Following functions calculates ROC for all tickers in our list, compares ASK vs LTP prices, and returns the stock ticker which satisfies all our criterias.
def ROC(ask, timeframe):
roc = []
if timeframe == 30:
rocs = (ask[ask.shape[0] - 1] - ask[0])/(ask[0])
else:
rocs = (ask[ask.shape[0] - 1] - ask[ask.shape[0] -2])/(ask[ask.shape[0] - 2])
roc.append(rocs)
return rocs
# Returns a list of most recent ROCs for all tickers
def return_ROC_list(tickers, timeframe):
ROC_tickers = []
for i in range(len(tickers)):
df = pd.read_csv('{}.csv'.format(tickers[i]))
df.set_index('timestamp', inplace= True)
df.index = pd.to_datetime(df.index, format ='%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
ROC_tickers.append(ROC(df['ask_price'], timeframe)) # [-1] forlast value (latest)
return ROC_tickers
# compared ASK vs LTP
def compare_ask_ltp(tickers, timeframe):
if len(tickers) != 0:
buy_stock = ''
ROCs = return_ROC_list(tickers, timeframe)
max_ROC = max(ROCs)
max_ROC_index = ROCs.index(max_ROC)
for i in range(len(tickers)):
if(len(tickers)==0):
break
buy_stock_init = tickers[max_ROC_index]
df = pd.read_csv('{}.csv'.format(buy_stock_init))
df.set_index('timestamp', inplace= True)
df.index = pd.to_datetime(df.index, format ='%Y-%m-%d').strftime('%Y-%m-%d %H:%M')
# list to keep track of number of ask_prices > price
buy_condition = []
ask_col = df.columns.get_loc('ask_price')
price_col = df.columns.get_loc('price')
for i in range(df.shape[0] - 2, df.shape[0]):
buy_condition.append(df.iloc[i, ask_col] > df.iloc[i,price_col])
if buy_condition[-1] == True:
buy_stock = buy_stock_init
return buy_stock
else:
tickers.pop(max_ROC_index)
ROCs.pop(max_ROC_index)
if(len(tickers)==0):
break
max_ROC = max(ROCs)
max_ROC_index = ROCs.index(max_ROC)
else: tickers = TICKERS
# returns which stock to buy
def stock_to_buy(tickers, timeframe):
entry_buy = compare_ask_ltp(tickers, timeframe)
return entry_buy
def algo(tickers):
# Calculates ROC
# Checks for stock with highest ROC and if ask_price > price
# Returns ticker to buy
if os.path.isfile('FirstTrade.csv'):
timeframe = 1
else:
timeframe = 30
stock = stock_to_buy(tickers, timeframe)
return stock
- After we find the ticker which satisfies all the criterias, we place a buy/sell order depending on our market position.
If our market position is open (i.e we do not hold any stocks in our portfolio), we place a buy order.
If we hold a position (i.e we have a stock in our portfolio), we check for returns. If returns >= 2%, we sell the current stock and buy the stock that we just found out satisfies all our criterias.
def buy(stock_to_buy: str):
cashBalance = api.get_account().cash
price_stock = api.get_last_trade(str(stock_to_buy)).price
targetPositionSize = ((float(cashBalance)) / (price_stock)) # Calculates required position size
api.submit_order(str(stock_to_buy), targetPositionSize, "buy", "market", "day") # Market order to open position
mail_content = '''ALERT
BUY Order Placed for {}: {} Shares at ${}'''.format(stock_to_buy, targetPositionSize, price_stock)
df = pd.DataFrame()
df['Time'] = ((dt.now()).astimezone(timezone('America/New_York'))).strftime("%H:%M:%S")
df['Ticker'] = stock_to_buy
df['Type'] = 'buy'
df['Price'] = price_stock
df['Quantity'] = targetPositionSize
df['Total'] = targetPositionSize * price_stock
# df['Balance'] = api.get_account().cash
with open('Orders.csv', 'a') as f:
df.to_csv(f, header=f.tell()==0)
return mail_content
def sell(current_stock, stock_to_buy):
# sells current_stock
quantity = int(api.list_positions()[0].qty)
sell_price = api.get_last_trade(str(current_stock)).price
# api.submit_order(str(current_stock), quantity, 'sell', 'market', 'day')
api.close_position(str(current_stock))
mail_content = '''ALERT
SELL Order Placed for {}: {} Shares at ${}'''.format(current_stock, quantity, sell_price)
df = pd.DataFrame()
df['Time'] = ((dt.now()).astimezone(timezone('America/New_York'))).strftime("%H:%M:%S")
df['Ticker'] = current_stock
df['Type'] = 'sell'
df['Price'] = sell_price
df['Quantity'] = quantity
df['Total'] = quantity * sell_price
with open('Orders.csv', 'a') as f:
df.to_csv(f, header=f.tell()==0)
return mail_content
def check_rets(current_stock, stock_to_buy):
# checks returns for stock in portfolio (api.get_positions()[0].symbol)
buy_price = float(api.get_position(current_stock).avg_entry_price) * float(api.get_position(current_stock).qty)
current_price = float(api.get_position(current_stock).current_price) * float(api.get_position(current_stock).qty)
if ((current_price - buy_price)/(buy_price))* 100 >=2:
mail_content = sell(current_stock, stock_to_buy)
else:
mail_content = 0
return mail_content
def mail_alert(mail_content, sleep_time):
# The mail addresses and password
sender_address = 'sender_address@email.com'
sender_pass = 'sender_password'
receiver_address = 'receiver_address@email.com'
# Setup MIME
message = MIMEMultipart()
message['From'] = 'Trading Bot'
message['To'] = receiver_address
message['Subject'] = 'HFT Second-Bot'
# The body and the attachments for the mail
message.attach(MIMEText(mail_content, 'plain'))
# Create SMTP session for sending the mail
session = smtplib.SMTP('smtp.gmail.com', 587) # use gmail with port
session.starttls() # enable security
# login with mail_id and password
session.login(sender_address, sender_pass)
text = message.as_string()
session.sendmail(sender_address, receiver_address, text)
session.quit()
time.sleep(sleep_time)
NOTE: mail_alert function notifies the bot user via mail whenever an order is placed.
- main() function
def main():
while True:
if api.get_clock().is_open == True:
# check if we have made the first ever trade yet, if yes, timeframe = 1 min, else trade at 10:00 am
if os.path.isfile('FirstTrade.csv'):
get_minute_data(tickers)
stock_to_buy = algo(tickers)
if len(api.list_positions()) == 0:
mail_content = buy(stock_to_buy)
mail_alert(mail_content, 5)
continue
else:
current_stock = api.list_positions()[0].symbol
mail_content = check_rets(current_stock, stock_to_buy)
if mail_content == 0:
continue
if len(api.list_positions()) == 0:
mail_alert(mail_content, 0)
mail_content = buy(stock_to_buy)
mail_alert(mail_content, 5)
# time.sleep(5)
else:
get_past30_data(tickers)
stock_to_buy = algo(tickers)
mail_content = buy(stock_to_buy)
mail_alert(mail_content, 5)
df = pd.DataFrame()
df['First Stok'] = stock_to_buy
df.to_csv('FirstTrade.csv')
- Checks if FirstTrade.csv exists (i.e if we are using the bot for the first time). Exists if we've made the first trade, else not.
- If FirstTrade.csv does not exist, we check criterias for first 30 mins when market opens, find stock_to_buy using algo function, and place a buy order.
- If FirstTrade.csv exists, bot fetches data for 1-min timeframe, checks for criterias with algo function and saves as variable stock_to_buy. If we have a open position (len(api.list_positions()) == 0), bot places a buy order for stock_to_buy and sends a buy order mail alert to user. If we do not have an open position, bot checks for returns. If returns >= 2%, bot sells the current stock in portfolio and buys stock_to_buy, sends mail alert for sell and buy trades. If return is not >= 2%, bot repeats step 3.
- Building an Alpaca Trading Bot in 7 steps: https://alpaca.markets/learn/algorithmic-trading-bot-7-steps/
- Alpaca Trading API Guide: https://algotrading101.com/learn/alpaca-trading-api-guide/
