1.
매매를 위한 API는 다양합니다. 증권사가 매매를 위하여 제공하는 API도 있고 특정한 회사의 매매프로그램이 제공하는 API도 있습니다. 제가 서비스하고 있는 ZeroAOS도 두가지 API를 제공합니다. 아마 API를 제공하는 증권사들이 벤치마킹을 한다고 하면 Interactive Brokers입니다. 자주 소개하였던 증권사입니다.
Python을 기반으로 하여 Paper Trading 및 Live Trading을 제공하는 Quantopian도 IB API를 기반으로 합니다. 글을 읽어보면 IB API를 이용한 다양한 사례가 나옵니다. 그 중 재미있는 사례를 소개하려고 합니다.
먼저 이름도 거창한 고빈도매매 전략입니다.
A high-frequency trading model using Interactive Brokers API with pairs and mean-reversion in Python
현재까지 구현한 기능이라고 합니다.
Cash-neutral strategy with long-short position
Bootstrap the model with historical data to derive usable strategy parameters
Bootstrapping takes some time, we need to bridge historical data with recent tick data
Transforming inhomogenous to homogeneous time series of 1 second intervals
Selection of highly-correlated stock pairs
Using volatility ratio to detect trending, mean-reversion or Brownian motion
Fair valuation by using beta of average 5 minute look-back price window
Fair valuation of stock A against more than 1 security (stock B, C…) is possible
Trade decisions based on mean-reversion, volatility ratio and deviation from fair prices
그리고 전략을 만들 때 참고로 한 논문입니다. 어떤 글을 보니까 매매를 할 때 꼭 두가지 준비를 하라고 하더군요.
첫째 읽어라(Read)
둘째 가상거래를 해라(Paper Trading)
MIT – Developing high-frequency equities trading model @ http://dspace.mit.edu/handle/1721.1/59122 SMU – Profiting from mean-reverting yield-curve trading strategies @ http://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=3488&context=lkcsb_research
github에 공개했지만 전략소스를 소개합니다. Python으로 개발한 무척 긴 소스입니다.
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####################################### # Author: James Ma # Email stuff here: jamesmawm@gmail.com ####################################### import numpy as np import pandas as pd from ib.opt import ibConnection, message from ib.opt import Connection from datetime import datetime import time from time import strftime import matplotlib.dates as dates import ChartUtil from StrategyParams import StrategyParams from StockData import StockData from ibUtil import * conn = None stocks_data = [] errs_data = [] ticks_data = [] strategy_params = StrategyParams() bid_price, ask_price, last_price, ticker_id = 0, 0, 0, 0 MAXIMUM_TICKS_WINDOW = 60*5 MAXIMUM_ERRS_WINDOW_IN_TICKS = 100 EVALUATION_TIME_IN_SECONDS = 20 account_code = "" order_id = 0 A_bid_price, A_ask_price = 0, 0 # 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 # Note: These parameters are unrealistic at the moment. def perform_trade_logic(fair_prices, std_A): global strategy_params, stocks_data, conn, order_id, A_bid_price, A_ask_price # Use stock A as point of trade # In this example, use stock B as the opposite pair. # Otherwise, in normal cases use 'cheaper' of the either opposite pairs. stock_A_data = stocks_data[0] stock_B_data = stocks_data[1] position_A = stock_A_data.get_position() lt_stdev = stock_A_data.get_long_term_std() st_stdev = stock_A_data.get_short_term_std() volatility_ratio = st_stdev / lt_stdev is_A_overbought, is_A_oversold = get_is_overbought_or_oversold(fair_prices) stock_B_fair_price = fair_prices[1] stock_contract_A = stock_A_data.get_stock_contract() stock_contract_B = stock_B_data.get_stock_contract() # Output details to console. print ticker_id,")"\ , "b/a:", A_bid_price, ",", A_ask_price\ , "std:", round(std_A, 4)\ , "stderrs:", strategy_params.get_last_stdevs()\ , "prices:", fair_prices\ , "vr:", round(volatility_ratio, 3)\ , "ovrbght/sld:", "T" if is_A_overbought else "F", "T" if is_A_oversold else "F"\ , "pos:", position_A # TODOs: # - Identify mean-reverting regime, trending regime and structural breaks if position_A == 0: if volatility_ratio < 1.0 and is_A_overbought: # Short A Buy B print "=====================" print "TRADE 1: SELL A BUY B" print "=====================" qty = 100 order_sell_A = create_stock_order(qty, False) order_buy_B = create_stock_order(qty, True) conn.placeOrder(order_id, stock_contract_A, order_sell_A) stock_A_data.on_send_order(-qty) order_id += 1 conn.placeOrder(order_id, stock_contract_B, order_buy_B) stock_B_data.on_send_order(qty) order_id += 1 elif volatility_ratio > 1.5 and is_A_oversold: print "=====================" print "TRADE 2: BUY A SELL B" print "=====================" qty = 100 order_buy_A = create_stock_order(qty, True) order_sell_B = create_stock_order(qty, False) conn.placeOrder(order_id, stock_contract_A, order_buy_A) stock_A_data.on_send_order(qty) order_id += 1 conn.placeOrder(order_id, stock_contract_B, order_sell_B) stock_B_data.on_send_order(-qty) order_id += 1 elif position_A < 0: # Cover short position in A - Take Profit if volatility_ratio > 1.5 and is_A_oversold: print "=====================" print "TRADE 3: BUY A SELL B" print "=====================" qty = 100 order_buy_A = create_stock_order(qty, True) order_sell_B = create_stock_order(qty, False) conn.placeOrder(order_id, stock_contract_A, order_buy_A) stock_A_data.on_send_order(qty) order_id += 1 conn.placeOrder(order_id, stock_contract_B, order_sell_B) stock_B_data.on_send_order(-qty) order_id += 1 # Cover short position in A - Stop Loss #if vr > 1.5 and is_A_oversold: elif position_A > 0: # Cover long position in A - Take Profit if volatility_ratio < 1.0 and is_A_overbought: print "=====================" print "TRADE 4: SELL A BUY B" print "=====================" qty = 100 order_sell_A = create_stock_order(qty, False) order_buy_B = create_stock_order(qty, True) conn.placeOrder(order_id, stock_contract_A, order_sell_A) stock_A_data.on_send_order(-qty) order_id += 1 conn.placeOrder(order_id, stock_contract_B, order_buy_B) stock_B_data.on_send_order(qty) order_id += 1 def get_is_overbought_or_oversold(fair_prices): stock_A_price = fair_prices[0] is_A_oversold = True is_A_overbought = True for fair_price in fair_prices[1:]: if fair_price > stock_A_price: is_A_overbought = False else: is_A_oversold = False return is_A_overbought, is_A_oversold def on_tick(): global bid_price, ask_price, last_price, strategy_params, ticker_id global A_bid_price, A_ask_price precision = 5 beta = strategy_params.get_st_beta() fair_prices = [] curr_stdevs = [] std = 0 # Get fair prices and standard deviation of errors series_length = 0 for i, stockdata in enumerate(stocks_data): price_series = stockdata.get_short_term_prices() if price_series is None: fair_prices.append(None) else: series_length = len(price_series) most_recent_price = price_series[-1] if i==0: # The original first time series as point of comparison. pd_series = pd.Series(price_series) std = pd_series.pct_change().std()*100 fair_prices.append( round(most_recent_price, precision)) else: fair_price = most_recent_price * beta fair_prices.append( round(fair_price, precision)) original_series = stocks_data[0].get_short_term_prices() if original_series is not None: err_series = original_series - price_series * beta pd_err_series = pd.Series(err_series) curr_stdevs.append( round(pd_err_series.std(), precision) ) else: curr_stdevs.append(None) strategy_params.add_to_stdevs_series(curr_stdevs, series_length) # Re-evaluate strategy params every EVALUATION_TIME_IN_SECONDS if (strategy_params.is_evaluation_time_elapsed(EVALUATION_TIME_IN_SECONDS) #and std > 0.095 #and std < 0.11 ): # TODO: # - Store historical betas # - Re-evaluate on regime shifts # - Refine current method: To get new beta, use last price when A's standard deviations are at normal levels print "=== Beta re-evaluated === " st_means = [] for stock_data_object in stocks_data: st_mean = stock_data_object.get_short_term_prices()[-1] st_means.append(st_mean) st_betas = [st_means[0]/price for price in st_means] strategy_params.set_st_betas(st_betas) strategy_params.set_new_evaluation_time() if A_bid_price != 0 and A_ask_price != 0: perform_trade_logic(fair_prices, std) def process_historical_data(msg): print msg vwap = msg.WAP stock_index = msg.reqId if vwap != -1: date_time = msg.date #open = msg.open #high = msg.high close = msg.close #volume = msg.volume stocks_data[stock_index].add_historical_data_point(close, date_time) elif vwap == -1: stocks_data[stock_index].set_finished_storing() def process_portfolio_updates(msg): contract = msg.contract position = msg.position market_price = msg.marketPrice market_value = msg.marketValue average_cost = msg.averageCost unrealized_pnl = msg.unrealizedPNL realized_pnl = msg.realizedPNL account_name = msg.accountName global stocks_data for stock_data in stocks_data: if stock_data.get_stock_contract() == contract: stocks_data.update_position(position, market_price, market_value , average_cost, unrealized_pnl, realized_pnl , account_name) def logger(msg): if msg.typeName == DataType.MSG_TYPE_HISTORICAL_DATA: process_historical_data(msg) elif msg.typeName == DataType.MSG_TYPE_UPDATE_PORTFOLIO: process_portfolio_updates(msg) elif msg.typeName == DataType.MSG_TYPE_MANAGED_ACCOUNTS: global account_code account_code = msg.accountsList elif msg.typeName == DataType.MSG_TYPE_NEXT_ORDER_ID: global order_id order_id = msg.orderId else: print "logger: " , msg def tick_string_event(msg): this_ticker_id = msg.tickerId if msg.tickType == DataType.FIELD_LAST_TIMESTAMP: print this_ticker_id, ": ", "ts: ", msg.value else: print "notickstring: ", msg def tick_generic(msg): print "gen: ", msg def append_tick_data_to_series(date_obj, price, tick_series): dtnum = dates.date2num(date_obj) new_tick = np.array([dtnum, price]) is_replacement = False if tick_series is None: tick_series = np.array([new_tick]) else: last_dtnum = tick_series[-1][0] dt2 = dates.num2date(last_dtnum) # Replace with latest price if within same second. if (date_obj.replace(tzinfo=None) - dt2.replace(tzinfo=None)).seconds == 0 and date_obj.second == dt2.second: is_replacement = True tick_series[-1,1] = price else: tick_series = np.vstack([tick_series, new_tick]) return tick_series, is_replacement # Use previous tick interpolation in creating homogeneous time series def extend_ticks_on_other_series(stock_index, date_obj): global stocks_data for i, stock_data in enumerate(stocks_data): chart_ds = stock_data.get_historical_short_term_chart_data_set() tick_series = chart_ds.get_ticks() if i != stock_index: previous_price = chart_ds.get_most_recent_price() tick_series, is_replacement = append_tick_data_to_series(date_obj, previous_price, tick_series) chart_ds.set_ticks(tick_series) if tick_series is not None and len(tick_series) > MAXIMUM_TICKS_WINDOW: tick_series = tick_series[-MAXIMUM_TICKS_WINDOW:] chart_ds.set_ticks(tick_series) def get_tick_series_at_index(stock_index): global stocks_data stock_data = stocks_data[stock_index] if stock_data.get_is_bootstrap_completed(): # Use real-time data chart_ds = stock_data.get_historical_short_term_chart_data_set() tick_series = chart_ds.get_ticks() return tick_series else: # Use historical data global ticks_data tick_series = ticks_data[stock_index] return tick_series def append_tick_data(stock_index, date_obj, price): global stocks_data tick_series = get_tick_series_at_index(stock_index) tick_series, is_replacement = append_tick_data_to_series(date_obj, price, tick_series) stock_data = stocks_data[stock_index] if stock_data.get_is_bootstrap_completed(): # Use real-time data chart_ds = stock_data.get_historical_short_term_chart_data_set() chart_ds.set_ticks(tick_series) if not is_replacement: extend_ticks_on_other_series(stock_index, date_obj) else: # Use historical data ticks_data[stock_index] = tick_series def tick_event(msg): global ticks_data, bid_price, ask_price, last_price, ticker_id, strategy_params global A_ask_price, A_bid_price ticker_id = msg.tickerId if msg.typeName == DataType.MSG_TYPE_TICK_STRING: if msg.tickType == DataType.FIELD_LAST_TIMESTAMP: print ticker_id, ": ", " ts: ", msg.value return if msg.field == DataType.FIELD_BID_PRICE: #print ticker_id, ": ", "bid: ", msg.price bid_price = msg.price if ticker_id == 0: A_bid_price = bid_price elif msg.field == DataType.FIELD_ASK_PRICE: #print ticker_id, ": ", "ask: ", msg.price ask_price= msg.price if ticker_id == 0: A_ask_price = ask_price #elif msg.field == DataType.FIELD_BID_SIZE: # print ticker_id, ": ", "bidvol: ", msg.size #elif msg.field == DataType.FIELD_ASK_SIZE: # print ticker_id, ": ", "askvol: ", msg.size elif msg.field == DataType.FIELD_LAST_PRICE: #print ticker_id, ": ", "last: ", msg.price, "at", datetime.now() last_price= msg.price append_tick_data(ticker_id, datetime.now(), msg.price) if strategy_params.is_bootstrap_completed(): on_tick() #elif msg.field == DataType.FIELD_LAST_SIZE: # print ticker_id, ": ", "lastvol: ", msg.size #elif msg.field == DataType.FIELD_HIGH: # print ticker_id, ": ", "h: ", msg.price #elif msg.field == DataType.FIELD_LOW: # print ticker_id, ": ", "l: ", msg.price #elif msg.field == DataType.FIELD_VOLUME: # print ticker_id, ": ", "vol: ", msg.size #elif msg.field == DataType.FIELD_CLOSE_PRICE: # print ticker_id, ": ", "close: ", msg.price #else: # print "nomsg: ", msg # # Throw away data to keep the desired time window region # while lastime - xdata[0] > dates.minutes(minutes_in_window): # del xdata[0] # del askdata[0] # del biddata[0] def plot_stocks(strategy_parameters): global stocks_data ys = [] for stock_index, stock_data_object in enumerate(stocks_data): chart_data_set = stock_data_object.get_historical_short_term_chart_data_set() beta = strategy_params.get_st_beta_at_index(stock_index) prices = np.array(chart_data_set.get_prices()) impv_prices = prices * beta ys.append(impv_prices) x = chart_data_set.get_dates() stdevs = strategy_parameters.get_stdevs_series() ChartUtil.setup_plots(x, ys, stdevs) def update_charts(): global stocks_data, strategy_params, A_ask_price, A_bid_price ys = [] for stock_index, stock_data in enumerate(stocks_data): st_chart_ds = stock_data.get_historical_short_term_chart_data_set() beta = strategy_params.get_st_beta_at_index(stock_index) prices = st_chart_ds.get_prices()*beta ys.append(prices) ys2 = strategy_params.get_stdevs_series() ChartUtil.update_plot(ys, ys2, A_bid_price, A_ask_price) def request_historical_data(ibconn, stock_index, stock_contract, duration, bar_size): ibconn.reqHistoricalData(stock_index , stock_contract , strftime(DataType.DATE_TIME_FORMAT) , duration , bar_size , DataType.WHAT_TO_SHOW_TRADES , DataType.RTH_ALL , DataType.DATEFORMAT_STRING) time.sleep(1) def setup_stocks_data(stocks): global ticks_data, stocks_data, errs_data for stock in stocks: stock_contract = create_stock_contract(stock) stock_data = StockData(stock_contract) stocks_data.append(stock_data) ticks_data.append(None) errs_data.append(None) ticks_data = np.array(ticks_data) errs_data = np.array(errs_data) def boot_strap_long_term(conn): for stock_index, stock_data_object in enumerate(stocks_data): stock_data_object.set_is_storing_long_term() stock_contract = stock_data_object.get_stock_contract() request_historical_data(conn, stock_index, stock_contract , DataType.DURATION_1_DAY, DataType.BAR_SIZE_1_MIN) def boot_strap_short_term(conn): for stock_index, stock_data in enumerate(stocks_data): stock_contract = stock_data.get_stock_contract() stock_data.set_is_storing_short_term() request_historical_data(conn, stock_index, stock_contract , DataType.DURATION_1_MIN, DataType.BAR_SIZE_1_SEC) def calculate_params(stocks_data_arr): lt_means, st_means = [], [] lt_log_returns, st_log_returns = [], [] for stock_data_object in stocks_data_arr: lt_mean = stock_data_object.get_long_term_mean() st_mean = stock_data_object.get_short_term_mean() lt_means.append(lt_mean) st_means.append(st_mean) lt_prices = stock_data_object.get_long_term_prices() st_prices = stock_data_object.get_short_term_prices() lt_log_return_series = np.log([price/prev_price for price, prev_price in zip(lt_prices, lt_prices[1:])]) st_log_return_series = np.log([price/prev_price for price, prev_price in zip(st_prices, st_prices[1:])]) lt_log_returns.append(lt_log_return_series) st_log_returns.append(st_log_return_series) lt_betas = [lt_means[0]/price for price in lt_means] st_betas = [st_means[0]/price for price in st_means] # Correlations lt_corrs = [] base_lt_log_return = lt_log_returns[0] for lt_log_return in lt_log_returns[1:]: correlation_matrix = np.corrcoef(base_lt_log_return, lt_log_return) corr = correlation_matrix[1][0] corr_rounded = round(corr * 100, 3) lt_corrs.append(corr_rounded) st_corrs = [] base_st_log_return = st_log_returns[0] for st_log_return in st_log_returns[1:]: correlation_matrix = np.corrcoef(base_st_log_return, st_log_return) corr = correlation_matrix[1][0] corr_rounded = round(corr * 100, 3) st_corrs.append(corr_rounded) strategy_params.set_betas(st_betas, lt_betas) strategy_params.set_corrs(st_corrs, lt_corrs) def register_data_handlers(): global conn conn.registerAll(logger) conn.unregister(logger , message.tickSize , message.tickPrice , message.tickString , message.tickGeneric , message.tickOptionComputation) #conn.register(tick_string_event, message.tickString) conn.register(tick_event, message.tickPrice, message.tickSize) #conn.register(tick_generic, message.tickGeneric) def request_streaming_data(conn): for stock_index, stock_data_object in enumerate(stocks_data): stock_contract = stock_data_object.get_stock_contract() conn.reqMktData(stock_index , stock_contract , DataType.GENERIC_TICKS_NONE , DataType.SNAPSHOT_NONE) time.sleep(1) conn.reqAccountUpdates(True, account_code) def wait_for_boot_strap_lt_to_complete(): is_waiting = True while is_waiting: is_waiting = False for stock_data in stocks_data: is_stock_waiting = stock_data.is_waiting_for_storing() if is_stock_waiting: is_waiting = True if is_waiting: time.sleep(1) def wait_for_boot_strap_st_to_complete(): is_waiting = True while is_waiting: is_waiting = False for stock_data in stocks_data: is_stock_waiting = stock_data.is_waiting_for_storing() if is_stock_waiting: is_waiting = True if is_waiting: time.sleep(1) def print_elapsed_time(start_time): elapsed_time = time.time() - start_time print "Completed in %.3f seconds." % elapsed_time def truncate_tick_series(tick_series, min_length): return tick_series[-min_length:] def truncate_short_term_ticks_to_length(stocks_data_arr, min_length): for i, stock_data in enumerate(stocks_data_arr): chart_ds = stock_data.get_historical_short_term_chart_data_set() tick_series = chart_ds.get_ticks() tick_series = truncate_tick_series(tick_series, min_length) chart_ds.set_ticks(tick_series) return stocks_data_arr def bridge_historical_and_present_ticks(stocks_data_arr, ticks_data, end_time_obj): min_length = 0 for stock_index, stock_data in enumerate(stocks_data_arr): chart_ds = stock_data.get_historical_short_term_chart_data_set() ticks_series = ticks_data[stock_index] most_recent_price = chart_ds.get_most_recent_price() most_recent_date = chart_ds.get_most_recent_dt() most_recent_dt = dates.num2date(most_recent_date).replace(tzinfo=None) seconds_left = (end_time_obj - most_recent_dt).seconds stock_data.set_is_storing_short_term() for i in range(1, seconds_left+1): tick_date = most_recent_date + dates.seconds(i) next_tick_date = most_recent_date + dates.seconds(i+1) # Find price of tick series with same second if ticks_series is not None: recent_ticks_data = ticks_series[ticks_series[:,0] >= tick_date] if len(recent_ticks_data) != 0: recent_ticks_data = recent_ticks_data[recent_ticks_data[:,0] < next_tick_date] if len(recent_ticks_data) != 0: most_recent_price = recent_ticks_data[0][1] # Store price as stick new_tick = np.array([tick_date, most_recent_price]) chart_ds.add_tick_with_datetime_tick(new_tick) stock_data.set_finished_storing() ticks_data[stock_index] = None stock_data.set_bootstrap_is_completed() length = len(chart_ds.get_ticks()) if min_length==0 or length < min_length: min_length = length truncate_short_term_ticks_to_length(stocks_data_arr, min_length) # For debugging tick data def print_shapes(): global stocks_data print "============" for i, stock_data in enumerate(stocks_data): chart_ds = stock_data.get_historical_short_term_chart_data_set() price_series = chart_ds.get_ticks() print np.shape(price_series) print "------------" def cancel_market_data_request(): global stocks_data, conn for stock_index, stock_data in enumerate(stocks_data): conn.cancelMktData(stock_index) time.sleep(1) def main(): global conn print "HFT model started." # Use ibConnection() for TWS, or create connection for API Gateway #conn = ibConnection() conn = Connection.create(port=4001, clientId=101) register_data_handlers() conn.connect() # Input your stocks of interest stocks = ("C", "MS") setup_stocks_data(stocks) request_streaming_data(conn) print "Boot strapping..." start_time = time.time() boot_strap_long_term(conn) wait_for_boot_strap_lt_to_complete() boot_strap_short_term(conn) wait_for_boot_strap_st_to_complete() print_elapsed_time(start_time) strategy_params.set_bootstrap_completed() print "Calculating strategy parameters..." start_time = time.time() calculate_params(stocks_data) print_elapsed_time(start_time) print "Bridging historical data..." start_time = time.time() bridge_historical_and_present_ticks(stocks_data, ticks_data, datetime.now()) print_elapsed_time(start_time) print "Trading started." try: plot_stocks(strategy_params) while True: update_charts() time.sleep(1) except Exception, e: print "Cancelling...", cancel_market_data_request() print "Disconnecting..." conn.disconnect() time.sleep(1) print "Disconnected." if __name__ == '__main__': main() |
2.
또다른 소스입니다. IB API와 FIX를 연결하고자 하는 시도입니다.
Connecting to IB through FIX Using Python to Execute your Algorithms
FIX Engine은 QuickFIX를 사용하였습니다. QuickFIX Server에 접속하기 위하여 사용한 Python Client는 QuickFix python client입니다.
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import sys import os import time import thread import quickfix as fix import quickfix44 as fix44 from datetime import datetime import cPickle as p class MinInc(fix.DoubleField): def __init__(self, data = None): if data == None: fix.DoubleField.__init__(self, 6350) else: fix.DoubleField.__init__(self, 6350, data) class MinBr(fix.DoubleField): def __init__(self, data = None): if data == None: fix.DoubleField.__init__(self, 6351) else: fix.DoubleField.__init__(self, 6351, data) class YTM(fix.DoubleField): def __init__(self, data = None): if data == None: fix.DoubleField.__init__(self, 6360) else: fix.DoubleField.__init__(self, 6360, data) class YTW(fix.DoubleField): def __init__(self, data = None): if data == None: fix.DoubleField.__init__(self, 6361) else: fix.DoubleField.__init__(self, 6361, data) class SECURITY(): def __init__(self): self.Symbol="" self.MDEntryID="" self.MDUpdateAction="" self.MDEntryType="" self.MDEntryPx=0 self.MDEntrySize=0 self.MinQty=0 self.MinInc=0 # self.MinBR=0 # self.YTM=0 # self.YTW=0 # def __str__(self): return ''' Symbol is %s, MDEntryID is %s, MDUpdateAction is %s, MDEntryType is %s, MDEntryPx is %f, MDEntrySize is %f, MinQty is %f, MinInc is %f, MinBr is %f, YTM is %f, YTW is %f ''' % (self.Symbol, self.MDEntryID, self.MDUpdateAction, self.MDEntryType, self.MDEntryPx, self.MDEntrySize, self.MinQty, self.MinInc, self.MinBR, self.YTM, self.YTW) class Application (fix.Application): global securities securities = {"":SECURITY()} securities.clear() def onCreate (self, sessionID): self.sessionID = sessionID print ("Application created - session: " + sessionID.toString ()) def onLogon (self, sessionID): print "Logon", sessionID def onLogout (self, sessionID): print "Logout", sessionID def toAdmin (self, message, sessionID): pass def fromAdmin (self, message, sessionID): pass def fromApp (self, message, sessionID): self.onMessage(message, sessionID) print "IN", message def toApp (self, message, sessionID): print "OUT", message def run(self): print ''' input 1 to fill the snapshot, input 2 to quit ''' while True: input = raw_input() if input == '1': self.fillSnapShot() elif input == '2': break else: continue def fillSnapShot(self): home_path = os.getenv('USERPROFILE')+"\\snapshotfrompython.txt" f = file(home_path, "w") f.write('''symbol, entryid, updateaction, entrytype, price, size, minqty, mininc, minbr, ytm, ytw\n''') lst = list() for name, security in securities.items(): lst.append(security) lst.sort(key = lambda security:security.Symbol) for security in lst: str='''%s,%s,%s,%s,%f,%f,%f,%f,%f,%f,%f\n''' % (security.Symbol, security.MDEntryID, security.MDUpdateAction, security.MDEntryType, security.MDEntryPx, security.MDEntrySize, security.MinQty, security.MinInc, security.MinBR, security.YTM, security.YTW) f.write(str) #p.dump(address, f) f.close() if len(lst)>0: print "Write the snapshot to the file ", home_path, "succesfully" def onMessage(self, message, sessionID): print "OnMessage %s" % message msgType = fix.MsgType () message.getHeader ().getField (msgType) if (msgType.getValue () == "X"): print "MarketDataIncrementalRefresh %s" % message noMDEntries = fix.NoMDEntries() message.getField(noMDEntries) if (noMDEntries.getValue()!=1): print "NoMDEntries in MarketDataIncrementalRefresh is not 1!" return group = fix44.MarketDataIncrementalRefresh.NoMDEntries() message.getGroup(1, group); entryID = fix.MDEntryID() group.getField(entryID) action = fix.MDUpdateAction() group.getField(action); actionvalue = action.getValue() #0=New, 1=Update, 2=Delete) if (actionvalue=='2'): #delete if entryID.getValue() in securities: del securities[entryID.getValue()] return security = SECURITY() security.MDEntryID = entryID.getValue() security.MDUpdateAction = action.getValue() symbol = fix.Symbol() if(group.isSetField(symbol)): group.getField(symbol) security.Symbol = symbol.getValue() entryType = fix.MDEntryType() if(group.isSetField(entryType)): group.getField(entryType) security.MDEntryType = entryType.getValue() price = fix.MDEntryPx () if(group.isSetField(price)): group.getField(price) security.MDEntryPx = price.getValue() size = fix.MDEntrySize() if(group.isSetField(size)): group.getField(size) security.MDEntrySize = size.getValue() qty = fix.MinQty () if(group.isSetField(qty)): group.getField(qty) security.MinQty = qty.getValue() inc = MinInc () if(message.isSetField(inc)): message.getField(inc) security.MinInc = inc.getValue() br = MinBr () if(message.isSetField(br)): message.getField(br) security.MinBR = br.getValue() ytm = YTM () if(message.isSetField(ytm)): message.getField(ytm) security.YTM = ytm.getValue() ytw = YTW () if(message.isSetField(ytw)): message.getField(ytw) security.YTW = ytw.getValue(); print security securities[entryID.getValue()] = security def queryEnterOrder (self): print ("\nTradeCaptureReport (AE)\n") trade = fix.Message () trade.getHeader ().setField (fix.BeginString (fix.BeginString_FIX44)) trade.getHeader ().setField (fix.MsgType (fix.MsgType_TradeCaptureReport)) trade.setField (fix.TradeReportTransType (fix.TradeReportTransType_NEW)) # 487 trade.setField (fix.TradeReportID (self.genTradeReportID ())) # 571 trade.setField (fix.TrdSubType (4)) # 829 trade.setField (fix.SecondaryTrdType (2)) # 855 trade.setField (fix.Symbol ("MYSYMBOL")) # 55 trade.setField (fix.LastQty (22)) # 32 trade.setField (fix.LastPx (21.12)) # 31 trade.setField (fix.TradeDate ((datetime.now ().strftime ("%Y%m%d")))) # 75 trade.setField (fix.TransactTime ((datetime.now ().strftime ("%Y%m%d-%H:%M:%S.%f"))[:-3])) # 60 trade.setField (fix.PreviouslyReported (False)) # 570 group = fix44.TradeCaptureReport ().NoSides () group.setField (fix.Side (fix.Side_SELL)) # 54 group.setField (fix.OrderID (self.genOrderID ())) # 37 group.setField (fix.NoPartyIDs (1)) # 453 group.setField (fix.PartyIDSource (fix.PartyIDSource_PROPRIETARY_CUSTOM_CODE)) # 447 group.setField (fix.PartyID ("CLEARING")) # 448 group.setField (fix.PartyRole (fix.PartyRole_CLEARING_ACCOUNT)) # 452 trade.addGroup (group) group.setField (fix.Side (fix.Side_BUY)) # 54 group.setField (fix.OrderID (self.genOrderID ())) # 37 group.setField (fix.NoPartyIDs (1)) # 453 group.setField (fix.PartyIDSource (fix.PartyIDSource_PROPRIETARY_CUSTOM_CODE)) # 447 group.setField (fix.PartyID ("CLEARING")) # 448 group.setField (fix.PartyRole (fix.PartyRole_CLEARING_ACCOUNT)) # 452 trade.addGroup (group) fix.Session.sendToTarget (trade, self.sessionID) |
마지막은 IB API를 이용하여 시세데이타를 저장하고 재생하는 프로그램입니다. 앞서 프로그램은 Python으로 개발했지만 이것은 R로 개발되었습니다.
Market Data Recording and Playback with IBrokers and R
증권사가 제공하는 API를 이용하여 다양한 상상력을 발휘해보시길 바랍니다.
