def __init__(self, host='localhost', port=4001,
client_id=101, is_use_gateway=False, evaluation_time_secs=20,
resample_interval_secs='30s',
moving_window_period=dt.timedelta(hours=1)):
self.moving_window_period = moving_window_period
self.chart = Chart()
self.ib_util = IBUtil()
# Store parameters for this model
self.strategy_params = StrategyParameters(evaluation_time_secs,
resample_interval_secs)
self.stocks_data = {} # Dictionary storing StockData objects.
self.symbols = None # List of current symbols
self.account_code = ""
self.prices = None # Store last prices in a DataFrame
self.trade_qty = 0
self.order_id = 0
self.lock = threading.Lock()
# Use ibConnection() for TWS, or create connection for API Gateway
self.conn = ibConnection() if is_use_gateway else \
Connection.create(host=host, port=port, clientId=client_id)
self.__register_data_handlers(self.__on_tick_event,
self.__event_handler)
def __init__(self, host='localhost', port=4001,
client_id=101, is_use_gateway=False, evaluation_time_secs=20,
resample_interval_secs='30s',
moving_window_period=dt.timedelta(hours=1)):
self.moving_window_period = moving_window_period
self.chart = Chart()
self.ib_util = IBUtil()
# Store parameters for this model
self.strategy_params = StrategyParameters(evaluation_time_secs,
resample_interval_secs)
self.stocks_data = {} # Dictionary storing StockData objects.
self.symbols = None # List of current symbols
self.account_code = ""
self.prices = None # Store last prices in a DataFrame
self.trade_qty = 0
self.order_id = 0
self.lock = threading.Lock()
# Use ibConnection() for TWS, or create connection for API Gateway
self.conn = ibConnection() if is_use_gateway else \
Connection.create(host=host, port=port, clientId=client_id)
self.__register_data_handlers(self.__on_tick_event,
self.__event_handler)
def __init__(self, host='localhost', port=4001,
client_id=101, is_use_gateway=False, evaluation_time_secs=20,
resample_interval_secs='30s',
moving_window_period=dt.timedelta(hours=1)):
self.moving_window_period = moving_window_period
self.chart = Chart()
self.ib_util = IBUtil()
self.strategy_params = StrategyParameters(evaluation_time_secs,
resample_interval_secs)
self.stocks_data = {}
self.symbols = None
self.account_code = ""
self.prices = None
self.trade_qty = 0
self.order_id = 0
self.lock = threading.Lock()
self.conn = ibConnection() if is_use_gateway else \
Connection.create(host=host, port=port, clientId=client_id)
self.__register_data_handlers(self.__on_tick_event,
self.__event_handler)
class HFTModel:
def __init__(self, host='localhost', port=4001,
client_id=101, is_use_gateway=False, evaluation_time_secs=20,
resample_interval_secs='30s',
moving_window_period=dt.timedelta(hours=1)):
self.moving_window_period = moving_window_period
self.chart = Chart()
self.ib_util = IBUtil()
# Store parameters for this model
self.strategy_params = StrategyParameters(evaluation_time_secs,
resample_interval_secs)
self.stocks_data = {} # Dictionary storing StockData objects.
self.symbols = None # List of current symbols
self.account_code = ""
self.prices = None # Store last prices in a DataFrame
self.trade_qty = 0
self.order_id = 0
self.lock = threading.Lock()
# Use ibConnection() for TWS, or create connection for API Gateway
self.conn = ibConnection() if is_use_gateway else \
Connection.create(host=host, port=port, clientId=client_id)
self.__register_data_handlers(self.__on_tick_event,
self.__event_handler)
def __perform_trade_logic(self):
"""
This part is the 'secret-sauce' where actual trades takes place.
My take is that great experience, good portfolio construction,
and together with robust backtesting will make your strategy viable.
GOOD PORTFOLIO CONTRUCTION CAN SAVE YOU FROM BAD RESEARCH,
BUT BAD PORTFOLIO CONSTRUCTION CANNOT SAVE YOU FROM GREAT RESEARCH
This trade logic uses volatility ratio and beta as our indicators.
- volatility ratio > 1 :: uptrend, volatility ratio < 1 :: downtrend
- beta is calculated as: mean(price A) / mean(price B)
We use the assumption that prive levels will mean-revert.
Expected price A = beta x price B
Consider other methods of identifying our trade logic:
- current trend
- current regime
- detect structural breaks
"""
volatility_ratio = self.strategy_params.get_volatility_ratio()
is_up_trend, is_down_trend = volatility_ratio > 1, volatility_ratio < 1
is_overbought, is_oversold = self.__is_overbought_or_oversold()
# Our final trade signals
is_buy_signal, is_sell_signal = (is_up_trend and is_oversold), \
(is_down_trend and is_overbought)
# Use account position details
symbol_a = self.symbols[0]
position = self.stocks_data[symbol_a].position
is_position_closed, is_short, is_long = \
(position == 0), (position < 0), (position > 0)
upnl, rpnl = self.__calculate_pnls()
# Display to terminal dynamically
signal_text = \
"BUY" if is_buy_signal else "SELL" if is_sell_signal else "NONE"
console_output = '\r[%s] signal=%s, position=%s UPnL=%s RPnL=%s\r' % \
(dt.datetime.now(), signal_text, position, upnl, rpnl)
sys.stdout.write(console_output)
sys.stdout.flush()
if is_position_closed and is_sell_signal:
print "=================================="
print "OPEN SHORT POSIITON: SELL A BUY B"
print "=================================="
self.__place_spread_order(-self.trade_qty)
elif is_position_closed and is_buy_signal:
print "=================================="
print "OPEN LONG POSIITON: BUY A SELL B"
print "=================================="
self.__place_spread_order(self.trade_qty)
elif is_short and is_buy_signal:
print "=================================="
print "CLOSE SHORT POSITION: BUY A SELL B"
print "=================================="
self.__place_spread_order(self.trade_qty)
elif is_long and is_sell_signal:
print "=================================="
print "CLOSE LONG POSITION: SELL A BUY B"
print "=================================="
self.__place_spread_order(-self.trade_qty)
def __recalculate_strategy_parameters_at_interval(self):
"""
Consider re-evaluation of parameters on:
- regime shifts
- structural breaks
"""
if self.strategy_params.is_evaluation_time_elapsed():
self.__calculate_strategy_params()
self.strategy_params.set_new_evaluation_time()
print '[%s] === Beta re-evaluated ===' % dt.datetime.now()
def __calculate_strategy_params(self):
"""
Here, we are calculating beta and volatility ratio
for our signal indicators.
Consider calculating other statistics here:
- stddevs of errs
- correlations
- co-integration
"""
[symbol_a, symbol_b] = self.symbols
filled_prices = self.prices.fillna(method='ffill')
resampled = filled_prices.resample(
self.strategy_params.resample_interval_secs, fill_method='ffill')\
.dropna()
mean = resampled.mean()
beta = mean[symbol_a] / mean[symbol_b]
stddevs = resampled.pct_change().dropna().std()
volatility_ratio = stddevs[symbol_a] / stddevs[symbol_b]
self.strategy_params.add_indicators(beta, volatility_ratio)
def __register_data_handlers(self,
tick_event_handler,
universal_event_handler):
self.conn.registerAll(universal_event_handler)
self.conn.unregister(universal_event_handler,
ib_message_type.tickSize,
ib_message_type.tickPrice,
ib_message_type.tickString,
ib_message_type.tickGeneric,
ib_message_type.tickOptionComputation)
self.conn.register(tick_event_handler,
ib_message_type.tickPrice,
ib_message_type.tickSize)
def __init_stocks_data(self, symbols):
self.symbols = symbols
self.prices = pd.DataFrame(columns=symbols) # Init price storage
for stock_symbol in symbols:
contract = self.ib_util.create_stock_contract(stock_symbol)
self.stocks_data[stock_symbol] = StockData(contract)
def __request_streaming_data(self, ib_conn):
# Stream market data
for index, (key, stock_data) in enumerate(
self.stocks_data.iteritems()):
ib_conn.reqMktData(index,
stock_data.contract,
datatype.GENERIC_TICKS_NONE,
datatype.SNAPSHOT_NONE)
time.sleep(1)
# Stream account updates
ib_conn.reqAccountUpdates(True, self.account_code)
def __request_historical_data(self, ib_conn):
self.lock.acquire()
try:
for index, (key, stock_data) in enumerate(
self.stocks_data.iteritems()):
stock_data.is_storing_data = True
ib_conn.reqHistoricalData(
index,
stock_data.contract,
time.strftime(datatype.DATE_TIME_FORMAT),
datatype.DURATION_1_HR,
datatype.BAR_SIZE_5_SEC,
datatype.WHAT_TO_SHOW_TRADES,
datatype.RTH_ALL,
datatype.DATEFORMAT_STRING)
time.sleep(1)
finally:
self.lock.release()
def __wait_for_download_completion(self):
is_waiting = True
while is_waiting:
is_waiting = False
self.lock.acquire()
try:
for symbol in self.stocks_data.keys():
if self.stocks_data[symbol].is_storing_data:
is_waiting = True
finally:
self.lock.release()
if is_waiting:
time.sleep(1)
def __place_spread_order(self, qty):
[symbol_a, symbol_b] = self.symbols
self.__send_order(symbol_a, qty)
self.__send_order(symbol_b, -qty)
def __send_order(self, symbol, qty):
stock_data = self.stocks_data[symbol]
order = self.ib_util.create_stock_order(abs(qty), qty > 0)
self.conn.placeOrder(self.__generate_order_id(),
stock_data.contract,
order)
stock_data.add_to_position(qty)
def __generate_order_id(self):
next_order_id = self.order_id
self.order_id += 1
return next_order_id
def __is_overbought_or_oversold(self):
[symbol_a, symbol_b] = self.symbols
leg_a_last_price = self.prices[symbol_a].values[-1]
leg_b_last_price = self.prices[symbol_b].values[-1]
expected_leg_a_price = \
leg_b_last_price * self.strategy_params.get_beta()
is_overbought = \
leg_a_last_price < expected_leg_a_price # Cheaper than expected
is_oversold = \
leg_a_last_price > expected_leg_a_price # Higher than expected
return is_overbought, is_oversold
def __on_portfolio_update(self, msg):
for key, stock_data in self.stocks_data.iteritems():
if stock_data.contract.m_symbol == msg.contract.m_symbol:
stock_data.update_position(msg.position,
msg.marketPrice,
msg.marketValue,
msg.averageCost,
msg.unrealizedPNL,
msg.realizedPNL,
msg.accountName)
return
def __calculate_pnls(self):
upnl, rpnl = 0, 0
for key, stock_data in self.stocks_data.iteritems():
upnl += stock_data.unrealized_pnl
rpnl += stock_data.realized_pnl
return upnl, rpnl
def __event_handler(self, msg):
if msg.typeName == datatype.MSG_TYPE_HISTORICAL_DATA:
self.__on_historical_data(msg)
elif msg.typeName == datatype.MSG_TYPE_UPDATE_PORTFOLIO:
self.__on_portfolio_update(msg)
elif msg.typeName == datatype.MSG_TYPE_MANAGED_ACCOUNTS:
self.account_code = msg.accountsList
elif msg.typeName == datatype.MSG_TYPE_NEXT_ORDER_ID:
self.order_id = msg.orderId
else:
print msg
def __on_historical_data(self, msg):
print msg
ticker_index = msg.reqId
if msg.WAP == -1:
self.__on_historical_data_completed(ticker_index)
else:
self.__add_historical_data(ticker_index, msg)
def __on_historical_data_completed(self, ticker_index):
self.lock.acquire()
try:
symbol = self.symbols[ticker_index]
self.stocks_data[symbol].is_storing_data = False
finally:
self.lock.release()
def __add_historical_data(self, ticker_index, msg):
timestamp = dt.datetime.strptime(msg.date, datatype.DATE_TIME_FORMAT)
self.__add_market_data(ticker_index, timestamp, msg.close)
def __on_tick_event(self, msg):
ticker_id = msg.tickerId
field_type = msg.field
# Store information from last traded price
if field_type == datatype.FIELD_LAST_PRICE:
last_price = msg.price
self.__add_market_data(ticker_id, dt.datetime.now(), last_price)
self.__trim_data_series()
# Post-bootstrap - make trading decisions
if self.strategy_params.is_bootstrap_completed():
self.__recalculate_strategy_parameters_at_interval()
self.__perform_trade_logic()
self.__update_charts()
def __add_market_data(self, ticker_index, timestamp, price):
symbol = self.symbols[ticker_index]
self.prices.loc[timestamp, symbol] = float(price)
self.prices = self.prices.fillna(method='ffill') # Clear NaN values
self.prices.sort_index(inplace=True)
def __update_charts(self):
if len(self.prices) > 0 and len(self.strategy_params.indicators) > 0:
self.chart.display_chart(self.prices,
self.strategy_params.indicators)
def __trim_data_series(self):
cutoff_timestamp = dt.datetime.now() - self.moving_window_period
self.prices = self.prices[self.prices.index >= cutoff_timestamp]
self.strategy_params.trim_indicators_series(cutoff_timestamp)
@staticmethod
def __print_elapsed_time(start_time):
elapsed_time = time.time() - start_time
print "Completed in %.3f seconds." % elapsed_time
def __cancel_market_data_request(self):
for i, symbol in enumerate(self.symbols):
self.conn.cancelMktData(i)
time.sleep(1)
def start(self, symbols, trade_qty):
print "HFT model started."
self.trade_qty = trade_qty
self.conn.connect() # Get IB connection object
self.__init_stocks_data(symbols)
self.__request_streaming_data(self.conn)
print "Bootstrapping the model..."
start_time = time.time()
self.__request_historical_data(self.conn)
self.__wait_for_download_completion()
self.strategy_params.set_bootstrap_completed()
self.__print_elapsed_time(start_time)
print "Calculating strategy parameters..."
start_time = time.time()
self.__calculate_strategy_params()
self.__print_elapsed_time(start_time)
print "Trading started."
try:
self.__update_charts()
while True:
time.sleep(1)
except Exception, e:
print "Exception:", e
print "Cancelling...",
self.__cancel_market_data_request()
print "Disconnecting..."
self.conn.disconnect()
time.sleep(1)
print "Disconnected."
class HFTModel:
def __init__(self, host='localhost', port=4001,
client_id=101, is_use_gateway=False, evaluation_time_secs=20,
resample_interval_secs='30s',
moving_window_period=dt.timedelta(hours=1)):
self.moving_window_period = moving_window_period
self.chart = Chart()
self.ib_util = IBUtil()
# Store parameters for this model
self.strategy_params = StrategyParameters(evaluation_time_secs,
resample_interval_secs)
self.stocks_data = {} # Dictionary storing StockData objects.
self.symbols = None # List of current symbols
self.account_code = ""
self.prices = None # Store last prices in a DataFrame
self.trade_qty = 0
self.order_id = 0
self.lock = threading.Lock()
# Use ibConnection() for TWS, or create connection for API Gateway
self.conn = ibConnection() if is_use_gateway else \
Connection.create(host=host, port=port, clientId=client_id)
self.__register_data_handlers(self.__on_tick_event,
self.__event_handler)
def __perform_trade_logic(self):
"""
This part is the 'secret-sauce' where actual trades takes place.
My take is that great experience, good portfolio construction,
and together with robust backtesting will make your strategy viable.
GOOD PORTFOLIO CONTRUCTION CAN SAVE YOU FROM BAD RESEARCH,
BUT BAD PORTFOLIO CONSTRUCTION CANNOT SAVE YOU FROM GREAT RESEARCH
This trade logic uses volatility ratio and beta as our indicators.
- volatility ratio > 1 :: uptrend, volatility ratio < 1 :: downtrend
- beta is calculated as: mean(price A) / mean(price B)
We use the assumption that prive levels will mean-revert.
Expected price A = beta x price B
Consider other methods of identifying our trade logic:
- current trend
- current regime
- detect structural breaks
"""
volatility_ratio = self.strategy_params.get_volatility_ratio()
is_up_trend, is_down_trend = volatility_ratio > 1, volatility_ratio < 1
is_overbought, is_oversold = self.__is_overbought_or_oversold()
# Our final trade signals
is_buy_signal, is_sell_signal = (is_up_trend and is_oversold), \
(is_down_trend and is_overbought)
# Use account position details
symbol_a = self.symbols[0]
position = self.stocks_data[symbol_a].position
is_position_closed, is_short, is_long = \
(position == 0), (position < 0), (position > 0)
upnl, rpnl = self.__calculate_pnls()
# Display to terminal dynamically
signal_text = \
"BUY" if is_buy_signal else "SELL" if is_sell_signal else "NONE"
console_output = '\r[%s] signal=%s, position=%s UPnL=%s RPnL=%s\r' % \
(dt.datetime.now(), signal_text, position, upnl, rpnl)
sys.stdout.write(console_output)
sys.stdout.flush()
if is_position_closed and is_sell_signal:
print "=================================="
print "OPEN SHORT POSIITON: SELL A BUY B"
print "=================================="
self.__place_spread_order(-self.trade_qty)
elif is_position_closed and is_buy_signal:
print "=================================="
print "OPEN LONG POSIITON: BUY A SELL B"
print "=================================="
self.__place_spread_order(self.trade_qty)
elif is_short and is_buy_signal:
print "=================================="
print "CLOSE SHORT POSITION: BUY A SELL B"
print "=================================="
self.__place_spread_order(self.trade_qty)
elif is_long and is_sell_signal:
print "=================================="
print "CLOSE LONG POSITION: SELL A BUY B"
print "=================================="
self.__place_spread_order(-self.trade_qty)
def __recalculate_strategy_parameters_at_interval(self):
"""
Consider re-evaluation of parameters on:
- regime shifts
- structural breaks
"""
if self.strategy_params.is_evaluation_time_elapsed():
self.__calculate_strategy_params()
self.strategy_params.set_new_evaluation_time()
print '[%s] === Beta re-evaluated ===' % dt.datetime.now()
def __calculate_strategy_params(self):
"""
Here, we are calculating beta and volatility ratio
for our signal indicators.
Consider calculating other statistics here:
- stddevs of errs
- correlations
- co-integration
"""
[symbol_a, symbol_b] = self.symbols
filled_prices = self.prices.fillna(method='ffill')
resampled = filled_prices.resample(
self.strategy_params.resample_interval_secs, fill_method='ffill')\
.dropna()
mean = resampled.mean()
beta = mean[symbol_a] / mean[symbol_b]
stddevs = resampled.pct_change().dropna().std()
volatility_ratio = stddevs[symbol_a] / stddevs[symbol_b]
self.strategy_params.add_indicators(beta, volatility_ratio)
def __register_data_handlers(self,
tick_event_handler,
universal_event_handler):
self.conn.registerAll(universal_event_handler)
self.conn.unregister(universal_event_handler,
ib_message_type.tickSize,
ib_message_type.tickPrice,
ib_message_type.tickString,
ib_message_type.tickGeneric,
ib_message_type.tickOptionComputation)
self.conn.register(tick_event_handler,
ib_message_type.tickPrice,
ib_message_type.tickSize)
def __init_stocks_data(self, symbols):
self.symbols = symbols
self.prices = pd.DataFrame(columns=symbols) # Init price storage
for stock_symbol in symbols:
contract = self.ib_util.create_stock_contract(stock_symbol)
self.stocks_data[stock_symbol] = StockData(contract)
def __request_streaming_data(self, ib_conn):
# Stream market data
for index, (key, stock_data) in enumerate(
self.stocks_data.iteritems()):
ib_conn.reqMktData(index,
stock_data.contract,
datatype.GENERIC_TICKS_NONE,
datatype.SNAPSHOT_NONE)
time.sleep(1)
# Stream account updates
ib_conn.reqAccountUpdates(True, self.account_code)
def __request_historical_data(self, ib_conn):
self.lock.acquire()
try:
for index, (key, stock_data) in enumerate(
self.stocks_data.iteritems()):
stock_data.is_storing_data = True
ib_conn.reqHistoricalData(
index,
stock_data.contract,
time.strftime(datatype.DATE_TIME_FORMAT),
datatype.DURATION_1_HR,
datatype.BAR_SIZE_5_SEC,
datatype.WHAT_TO_SHOW_TRADES,
datatype.RTH_ALL,
datatype.DATEFORMAT_STRING)
time.sleep(1)
finally:
self.lock.release()
def __wait_for_download_completion(self):
is_waiting = True
while is_waiting:
is_waiting = False
self.lock.acquire()
try:
for symbol in self.stocks_data.keys():
if self.stocks_data[symbol].is_storing_data:
is_waiting = True
finally:
self.lock.release()
if is_waiting:
time.sleep(1)
def __place_spread_order(self, qty):
[symbol_a, symbol_b, symbol_c] = self.symbols
self.__send_order(symbol_a, qty)
time.sleep(3)
self.__send_order(symbol_b, qty)
time.sleep(3)
self.__send_order(symbol_c, -qty)
time.sleep(3)
def __send_order(self, symbol, qty):
stock_data = self.stocks_data[symbol]
order = self.ib_util.create_stock_order(abs(qty), qty > 0)
self.conn.placeOrder(self.__generate_order_id(),
stock_data.contract,
order)
stock_data.add_to_position(qty)
def __generate_order_id(self):
next_order_id = self.order_id
self.order_id += 1
return next_order_id
def __is_overbought_or_oversold(self):
[symbol_a, symbol_b] = self.symbols
leg_a_last_price = self.prices[symbol_a].values[-1]
leg_b_last_price = self.prices[symbol_b].values[-1]
expected_leg_a_price = \
leg_b_last_price * self.strategy_params.get_beta()
is_overbought = \
leg_a_last_price < expected_leg_a_price # Cheaper than expected
is_oversold = \
leg_a_last_price > expected_leg_a_price # Higher than expected
return is_overbought, is_oversold
def __on_portfolio_update(self, msg):
for key, stock_data in self.stocks_data.iteritems():
if stock_data.contract.m_symbol == msg.contract.m_symbol:
stock_data.update_position(msg.position,
msg.marketPrice,
msg.marketValue,
msg.averageCost,
msg.unrealizedPNL,
msg.realizedPNL,
msg.accountName)
return
def __calculate_pnls(self):
upnl, rpnl = 0, 0
for key, stock_data in self.stocks_data.iteritems():
upnl += stock_data.unrealized_pnl
rpnl += stock_data.realized_pnl
return upnl, rpnl
def __event_handler(self, msg):
if msg.typeName == datatype.MSG_TYPE_HISTORICAL_DATA:
self.__on_historical_data(msg)
elif msg.typeName == datatype.MSG_TYPE_UPDATE_PORTFOLIO:
self.__on_portfolio_update(msg)
elif msg.typeName == datatype.MSG_TYPE_MANAGED_ACCOUNTS:
self.account_code = msg.accountsList
elif msg.typeName == datatype.MSG_TYPE_NEXT_ORDER_ID:
self.order_id = msg.orderId
else:
print msg
def __on_historical_data(self, msg):
print msg
ticker_index = msg.reqId
if msg.WAP == -1:
self.__on_historical_data_completed(ticker_index)
else:
self.__add_historical_data(ticker_index, msg)
def __on_historical_data_completed(self, ticker_index):
self.lock.acquire()
try:
symbol = self.symbols[ticker_index]
self.stocks_data[symbol].is_storing_data = False
finally:
self.lock.release()
def __add_historical_data(self, ticker_index, msg):
timestamp = dt.datetime.strptime(msg.date, datatype.DATE_TIME_FORMAT)
self.__add_market_data(ticker_index, timestamp, msg.close)
def __on_tick_event(self, msg):
ticker_id = msg.tickerId
field_type = msg.field
# Store information from last traded price
if field_type == datatype.FIELD_LAST_PRICE:
last_price = msg.price
self.__add_market_data(ticker_id, dt.datetime.now(), last_price)
self.__trim_data_series()
# Post-bootstrap - make trading decisions
if self.strategy_params.is_bootstrap_completed():
self.__recalculate_strategy_parameters_at_interval()
self.__perform_trade_logic()
self.__update_charts()
def __add_market_data(self, ticker_index, timestamp, price):
symbol = self.symbols[ticker_index]
self.prices.loc[timestamp, symbol] = float(price)
self.prices = self.prices.fillna(method='ffill') # Clear NaN values
self.prices.sort_index(inplace=True)
def __update_charts(self):
if len(self.prices) > 0 and len(self.strategy_params.indicators) > 0:
self.chart.display_chart(self.prices,
self.strategy_params.indicators)
def __trim_data_series(self):
cutoff_timestamp = dt.datetime.now() - self.moving_window_period
self.prices = self.prices[self.prices.index >= cutoff_timestamp]
self.strategy_params.trim_indicators_series(cutoff_timestamp)
@staticmethod
def __print_elapsed_time(start_time):
elapsed_time = time.time() - start_time
print "Completed in %.3f seconds." % elapsed_time
def __cancel_market_data_request(self):
for i, symbol in enumerate(self.symbols):
self.conn.cancelMktData(i)
time.sleep(1)
def start(self, symbols, trade_qty):
print "HFT model started."
self.trade_qty = trade_qty
self.conn.connect() # Get IB connection object
self.__init_stocks_data(symbols)
self.__request_streaming_data(self.conn)
print "Bootstrapping the model..."
start_time = time.time()
self.__request_historical_data(self.conn)
self.__wait_for_download_completion()
self.strategy_params.set_bootstrap_completed()
self.__print_elapsed_time(start_time)
print "Calculating strategy parameters..."
start_time = time.time()
self.__calculate_strategy_params()
self.__print_elapsed_time(start_time)
print "Trading started."
try:
self.__update_charts()
while True:
time.sleep(1)
except Exception, e:
print "Exception:", e
print "Cancelling...",
self.__cancel_market_data_request()
print "Disconnecting..."
self.conn.disconnect()
time.sleep(1)
print "Disconnected."
class ATS:
def __init__(self, host='localhost', port=4001,
client_id=101, is_use_gateway=False, evaluation_time_secs=20,
resample_interval_secs='30s',
moving_window_period=dt.timedelta(hours=1)):
self.moving_window_period = moving_window_period
self.chart = Chart()
self.ib_util = IBUtil()
self.strategy_params = StrategyParameters(evaluation_time_secs,
resample_interval_secs)
self.stocks_data = {}
self.symbols = None
self.account_code = ""
self.prices = None
self.trade_qty = 0
self.order_id = 0
self.lock = threading.Lock()
self.conn = ibConnection() if is_use_gateway else \
Connection.create(host=host, port=port, clientId=client_id)
self.__register_data_handlers(self.__on_tick_event,
self.__event_handler)
def __perform_trade_logic(self):
volatility_ratio = self.strategy_params.get_volatility_ratio()
is_up_trend, is_down_trend = volatility_ratio > 1, volatility_ratio < 1
is_overbought, is_oversold = self.__is_overbought_or_oversold()
is_buy_signal, is_sell_signal = (is_up_trend and is_oversold), \
(is_down_trend and is_overbought)
symbol_a = self.symbols[0]
position = self.stocks_data[symbol_a].position
is_position_closed, is_short, is_long = \
(position == 0), (position < 0), (position > 0)
upnl, rpnl = self.__calculate_pnls()
signal_text = \
"BUY" if is_buy_signal else "SELL" if is_sell_signal else "NONE"
console_output = '\r[%s] signal=%s, position=%s UPnL=%s RPnL=%s\r' % \
(dt.datetime.now(), signal_text, position, upnl, rpnl)
sys.stdout.write(console_output)
sys.stdout.flush()
if is_position_closed and is_sell_signal:
print "=================================="
print "OPEN SHORT POSIITON: SELL A BUY B"
print "=================================="
self.__place_spread_order(-self.trade_qty)
elif is_position_closed and is_buy_signal:
print "=================================="
print "OPEN LONG POSIITON: BUY A SELL B"
print "=================================="
self.__place_spread_order(self.trade_qty)
elif is_short and is_buy_signal:
print "=================================="
print "CLOSE SHORT POSITION: BUY A SELL B"
print "=================================="
self.__place_spread_order(self.trade_qty)
elif is_long and is_sell_signal:
print "=================================="
print "CLOSE LONG POSITION: SELL A BUY B"
print "=================================="
self.__place_spread_order(-self.trade_qty)
def __recalculate_strategy_parameters_at_interval(self):
if self.strategy_params.is_evaluation_time_elapsed():
self.__calculate_strategy_params()
self.strategy_params.set_new_evaluation_time()
print '[%s] === Beta re-evaluated ===' % dt.datetime.now()
def __calculate_strategy_params(self):
[symbol_a, symbol_b] = self.symbols
filled_prices = self.prices.fillna(method='ffill')
resampled = filled_prices.resample(
self.strategy_params.resample_interval_secs, fill_method='ffill')\
.dropna()
mean = resampled.mean()
beta = mean[symbol_a] / mean[symbol_b]
stddevs = resampled.pct_change().dropna().std()
volatility_ratio = stddevs[symbol_a] / stddevs[symbol_b]
self.strategy_params.add_indicators(beta, volatility_ratio)
def __register_data_handlers(self,
tick_event_handler,
universal_event_handler):
self.conn.registerAll(universal_event_handler)
self.conn.unregister(universal_event_handler,
ib_message_type.tickSize,
ib_message_type.tickPrice,
ib_message_type.tickString,
ib_message_type.tickGeneric,
ib_message_type.tickOptionComputation)
self.conn.register(tick_event_handler,
ib_message_type.tickPrice,
ib_message_type.tickSize)
def __init_stocks_data(self, symbols):
self.symbols = symbols
self.prices = pd.DataFrame(columns=symbols)
for stock_symbol in symbols:
contract = self.ib_util.create_stock_contract(stock_symbol)
self.stocks_data[stock_symbol] = StockData(contract)
def __request_streaming_data(self, ib_conn):
for index, (key, stock_data) in enumerate(
self.stocks_data.iteritems()):
ib_conn.reqMktData(index,
stock_data.contract,
datatype.GENERIC_TICKS_NONE,
datatype.SNAPSHOT_NONE)
time.sleep(1)
ib_conn.reqAccountUpdates(True, self.account_code)
def __request_historical_data(self, ib_conn):
self.lock.acquire()
try:
for index, (key, stock_data) in enumerate(
self.stocks_data.iteritems()):
stock_data.is_storing_data = True
ib_conn.reqHistoricalData(
index,
stock_data.contract,
time.strftime(datatype.DATE_TIME_FORMAT),
datatype.DURATION_1_HR,
datatype.BAR_SIZE_5_SEC,
datatype.WHAT_TO_SHOW_TRADES,
datatype.RTH_ALL,
datatype.DATEFORMAT_STRING)
time.sleep(1)
finally:
self.lock.release()
def __wait_for_download_completion(self):
is_waiting = True
while is_waiting:
is_waiting = False
self.lock.acquire()
try:
for symbol in self.stocks_data.keys():
if self.stocks_data[symbol].is_storing_data:
is_waiting = True
finally:
self.lock.release()
if is_waiting:
time.sleep(1)
def __place_spread_order(self, qty):
[symbol_a, symbol_b] = self.symbols
self.__send_order(symbol_a, qty)
self.__send_order(symbol_b, -qty)
def __send_order(self, symbol, qty):
stock_data = self.stocks_data[symbol]
order = self.ib_util.create_stock_order(abs(qty), qty > 0)
self.conn.placeOrder(self.__generate_order_id(),
stock_data.contract,
order)
stock_data.add_to_position(qty)
def __generate_order_id(self):
next_order_id = self.order_id
self.order_id += 1
return next_order_id
def __is_overbought_or_oversold(self):
[symbol_a, symbol_b] = self.symbols
leg_a_last_price = self.prices[symbol_a].values[-1]
leg_b_last_price = self.prices[symbol_b].values[-1]
expected_leg_a_price = \
leg_b_last_price * self.strategy_params.get_beta()
is_overbought = \
leg_a_last_price < expected_leg_a_price
is_oversold = \
leg_a_last_price > expected_leg_a_price
return is_overbought, is_oversold
def __on_portfolio_update(self, msg):
for key, stock_data in self.stocks_data.iteritems():
if stock_data.contract.m_symbol == msg.contract.m_symbol:
stock_data.update_position(msg.position,
msg.marketPrice,
msg.marketValue,
msg.averageCost,
msg.unrealizedPNL,
msg.realizedPNL,
msg.accountName)
return
def __calculate_pnls(self):
upnl, rpnl = 0, 0
for key, stock_data in self.stocks_data.iteritems():
upnl += stock_data.unrealized_pnl
rpnl += stock_data.realized_pnl
return upnl, rpnl
def __event_handler(self, msg):
if msg.typeName == datatype.MSG_TYPE_HISTORICAL_DATA:
self.__on_historical_data(msg)
elif msg.typeName == datatype.MSG_TYPE_UPDATE_PORTFOLIO:
self.__on_portfolio_update(msg)
elif msg.typeName == datatype.MSG_TYPE_MANAGED_ACCOUNTS:
self.account_code = msg.accountsList
elif msg.typeName == datatype.MSG_TYPE_NEXT_ORDER_ID:
self.order_id = msg.orderId
else:
print msg
def __on_historical_data(self, msg):
print msg
ticker_index = msg.reqId
if msg.WAP == -1:
self.__on_historical_data_completed(ticker_index)
else:
self.__add_historical_data(ticker_index, msg)
def __on_historical_data_completed(self, ticker_index):
self.lock.acquire()
try:
symbol = self.symbols[ticker_index]
self.stocks_data[symbol].is_storing_data = False
finally:
self.lock.release()
def __add_historical_data(self, ticker_index, msg):
timestamp = dt.datetime.strptime(msg.date, datatype.DATE_TIME_FORMAT)
self.__add_market_data(ticker_index, timestamp, msg.close)
def __on_tick_event(self, msg):
ticker_id = msg.tickerId
field_type = msg.field
if field_type == datatype.FIELD_LAST_PRICE:
last_price = msg.price
self.__add_market_data(ticker_id, dt.datetime.now(), last_price)
self.__trim_data_series()
if self.strategy_params.is_bootstrap_completed():
self.__recalculate_strategy_parameters_at_interval()
self.__perform_trade_logic()
self.__update_charts()
def __add_market_data(self, ticker_index, timestamp, price):
symbol = self.symbols[ticker_index]
self.prices.loc[timestamp, symbol] = float(price)
self.prices = self.prices.fillna(method='ffill')
self.prices.sort_index(inplace=True)
def __update_charts(self):
if len(self.prices) > 0 and len(self.strategy_params.indicators) > 0:
self.chart.display_chart(self.prices,
self.strategy_params.indicators)
def __trim_data_series(self):
cutoff_timestamp = dt.datetime.now() - self.moving_window_period
self.prices = self.prices[self.prices.index >= cutoff_timestamp]
self.strategy_params.trim_indicators_series(cutoff_timestamp)
@staticmethod
def __print_elapsed_time(start_time):
elapsed_time = time.time() - start_time
print "Completed in %.3f seconds." % elapsed_time
def __cancel_market_data_request(self):
for i, symbol in enumerate(self.symbols):
self.conn.cancelMktData(i)
time.sleep(1)
def start(self, symbols, trade_qty):
print "ATS model started."
self.trade_qty = trade_qty
self.conn.connect()
self.__init_stocks_data(symbols)
self.__request_streaming_data(self.conn)
print "Bootstrapping the model..."
start_time = time.time()
self.__request_historical_data(self.conn)
self.__wait_for_download_completion()
self.strategy_params.set_bootstrap_completed()
self.__print_elapsed_time(start_time)
print "Calculating strategy parameters..."
start_time = time.time()
self.__calculate_strategy_params()
self.__print_elapsed_time(start_time)
print "Trading started."
try:
self.__update_charts()
while True:
time.sleep(1)
except Exception, e:
print "Exception:", e
print "Cancelling...",
self.__cancel_market_data_request()
print "Disconnecting..."
self.conn.disconnect()
time.sleep(1)
print "Disconnected."