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Add support for additional market data bar types (#61)
* Add support for additional market data bar types * clarify ports & add comments
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‎examples/example_all_functionality.py‎

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@@ -299,12 +299,38 @@ def get_contracts() -> Dict[str, Contract]:
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bar_type=dhib.BarDataType.OPTION_IMPLIED_VOLATILITY, keep_up_to_date=False)
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client.request_bars_historical(rc, duration=dhib.Duration.days(10), bar_size=dhib.BarSize.MIN_5,
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bar_type=dhib.BarDataType.TRADES)
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client.request_bars_historical(rc, duration=dhib.Duration.days(10), bar_size=dhib.BarSize.MIN_5,
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bar_type=dhib.BarDataType.ADJUSTED_LAST, keep_up_to_date=False)
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client.request_bars_realtime(rc, bar_type=dhib.BarDataType.MIDPOINT)
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client.request_bars_realtime(rc, bar_type=dhib.BarDataType.BID)
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client.request_bars_realtime(rc, bar_type=dhib.BarDataType.ASK)
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client.request_bars_realtime(rc, bar_type=dhib.BarDataType.TRADES)
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print("==============================================================================================================")
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print("==== Request bars (bonds).")
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print("==============================================================================================================")
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# enter CUSIP as symbol
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contract = Contract()
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contract.symbol = "IBCID411964960"
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contract.secType = "BOND"
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contract.exchange = "SMART"
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contract.currency = "USD"
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rc = client.get_registered_contract(contract)
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print(contract)
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client.request_bars_historical(rc, duration=dhib.Duration.days(22), bar_size=dhib.BarSize.DAY_1,
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bar_type=dhib.BarDataType.YIELD_BID, keep_up_to_date=False)
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client.request_bars_historical(rc, duration=dhib.Duration.days(22), bar_size=dhib.BarSize.DAY_1,
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bar_type=dhib.BarDataType.YIELD_ASK, keep_up_to_date=False)
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client.request_bars_historical(rc, duration=dhib.Duration.days(22), bar_size=dhib.BarSize.DAY_1,
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bar_type=dhib.BarDataType.YIELD_BID_ASK, keep_up_to_date=False)
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client.request_bars_historical(rc, duration=dhib.Duration.days(22), bar_size=dhib.BarSize.DAY_1,
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bar_type=dhib.BarDataType.YIELD_LAST, keep_up_to_date=False)
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print("==============================================================================================================")
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print("==== Request tick data.")
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print("==============================================================================================================")

‎examples/example_beta_calc.py‎

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## Set the API port. Default port numbers are:
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# 7496 - Trader Workstation, real trading
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# 4001 - IB Gateway, real trading
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# 7497 - Trader Workstation, paper trading
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# 4002 - IB Gateway, paper trading
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API_PORT = 7497
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import deephaven_ib as dhib
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# Disable read-only mode when connecting to the default ports for paper trading:
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if API_PORT == 7497 or API_PORT == 4002:
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read_only_api = False
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else:
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read_only_api = True
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client = dhib.IbSessionTws(host="host.docker.internal", port=API_PORT, read_only=read_only_api)
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client.connect()
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if client.is_connected():
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print('Client connected!')
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else:
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raise RuntimeError("Client not connected!")
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def check_table_size(dh_table, table_name, expected_size=1):
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table_size = dh_table.size
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if (table_size < expected_size):
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raise RuntimeError(
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'Table "' + table_name + '" has ' + str(table_size) + ' rows! (Expected ' + str(expected_size) + '.)')
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else:
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print('Found ' + str(table_size) + ' rows in table "' + table_name + '".')
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# Get the Deephaven table of position updates, and use 'last_by' to find the
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# current positions (i.e. last row for each ContractId):
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positions = client.tables['accounts_positions'].last_by(['ContractId'])
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positions.j_table.awaitUpdate()
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check_table_size(positions, "pos")
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##########
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##########
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##########
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import numpy as np
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from deephaven.pandas import to_pandas
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# Get a DH table containing only the distinct Symbols:
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pos_syms = positions.select_distinct(['Symbol'])
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mkt_data_syms_set = set(to_pandas(pos_syms)['Symbol'].values)
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print('Found ' + str(len(mkt_data_syms_set)) + ' position symbols: ' + str(mkt_data_syms_set))
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# Add SPY to the set of symbols to request data for:
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mkt_data_syms_set.add('SPY')
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from ibapi.contract import Contract
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c = Contract()
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c.secType = 'STK'
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c.exchange = 'SMART'
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c.currency = 'USD'
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c.symbol = None
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for sym in mkt_data_syms_set:
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print('Requesting data for symbol=' + str(sym))
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c.symbol = sym
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rc = client.get_registered_contract(c)
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client.request_bars_historical(
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rc,
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duration=dhib.Duration.days(253),
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bar_size=dhib.BarSize.DAY_1,
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bar_type=dhib.BarDataType.ADJUSTED_LAST,
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keep_up_to_date=False
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)
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# Retrieve the Deephaven table of historical data bars:
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hist_data_bars = client.tables['bars_historical']
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# Wait for data to be retrieved:
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from time import sleep
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sleep(5)
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hist_data_bars.j_table.awaitUpdate()
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hist_data_recvd_syms = hist_data_bars.select_distinct(['Symbol'])
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check_table_size(hist_data_recvd_syms, 'hist_data_recvd_syms', len(mkt_data_syms_set))
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##########
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##########
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##########
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# Use 'colname_[i-1]' to read a value from the previous row
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hist_data_with_return = hist_data_bars \
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.update_view(formulas=[
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'SameTickerAsPrevRow = Symbol=Symbol_[i-1]',
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'Last = !SameTickerAsPrevRow ? null : Close_[i-1]',
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'Chg = Close - Last',
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'Return = Chg/Last',
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])
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# Join the SPY returns onto the returns for all stocks
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spy = hist_data_with_return.where("Symbol=`SPY`")
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hist_data_with_spy = hist_data_with_return.natural_join(spy, ['Timestamp'], ['SPY_Return=Return'])
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##########
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##########
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##########
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# Install sklearn and run a linear regression to calculate betas
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print("Installing sklearn...")
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import os
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os.system("pip install sklearn")
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from sklearn.linear_model import LinearRegression
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## Use a DynamicTableWriter to store regression results in a Deephaven table
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import deephaven.dtypes as dht
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from deephaven import DynamicTableWriter
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from deephaven.table import Table
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table_writer = DynamicTableWriter(
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{"Symbol": dht.string,
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"Beta": dht.double,
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"Intercept": dht.double,
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"R2": dht.double
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}
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)
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regression_results = table_writer.table
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# Partition the table, creating a distinct table for each Symbol:
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data_partitioned = hist_data_with_spy.partition_by(['Symbol'])
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print('Calculating betas...')
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for symbol in mkt_data_syms_set:
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print('Calculating beta for ' + symbol + '...')
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returns_for_betas = data_partitioned.get_constituent(symbol) \
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.where(['!isNull(Return)', '!isNull(SPY_Return)'])
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returns_for_betas_df = to_pandas(returns_for_betas)
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reg = LinearRegression()
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X = returns_for_betas_df['SPY_Return'].values.reshape(-1, 1)
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Y = returns_for_betas_df['Return']
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reg.fit(X, Y)
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r2 = reg.score(X, Y).real
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print(symbol + ' coef: ' + str(reg.coef_) +
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'; intercept: ' + str(reg.intercept_) +
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'; R2: ', str(r2))
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# Append to the 'regression_results' table:
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table_writer.write_row(
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symbol,
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reg.coef_[0],
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reg.intercept_,
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r2
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)
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print('Finished calculating betas!')
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##########
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##########
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##########
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# Request live prices:
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ticks_price = client.tables['ticks_price']
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live_prices = ticks_price.last_by(['ContractId'])
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for sym in mkt_data_syms_set:
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print('Requesting data for symbol=' + str(sym))
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c.symbol = sym
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rc = client.get_registered_contract(c)
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client.request_market_data(
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rc,
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snapshot=False
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)
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sleep(2)
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live_prices.j_table.awaitUpdate()
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check_table_size(live_prices, 'live_prices', len(mkt_data_syms_set))
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##########
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##########
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##########
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# Join the table of betas onto the positions
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pos_with_beta = positions.natural_join(live_prices, ['ContractId'], ['Price']) \
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.natural_join(regression_results, ['Symbol'], ['Beta', 'R2']) \
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.view([
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'Symbol',
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'ContractId',
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'SecType',
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'Currency',
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'Position',
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'PosValue = Position * Price',
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'Price',
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'AvgCost',
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'PNL = PosValue - AvgCost * Position',
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'Beta',
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'R2',
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'SPYBetaValue = Beta * PosValue',
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])
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##########
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##########
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##########
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# Calculate hedge, excluding positions with a very low R2:
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hedge_shares = pos_with_beta \
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.view([
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'PosValue',
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'WeightedBeta = Beta * PosValue',
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'SPYBetaValue',
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'SPYBetaValueForHedge = R2 > 1/5 ? SPYBetaValue : 0'
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]) \
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.sum_by() \
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.natural_join(live_prices.where('Symbol=`SPY`'), [], ['SPY_Price=Price']) \
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.view([
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'PortfolioValue = PosValue',
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'PortfolioBeta = WeightedBeta / PosValue',
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'SPYBetaValue',
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'SPYBetaValueForHedge',
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'HedgeShares = -round(SPYBetaValueForHedge / SPY_Price)',
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'HedgeCost = HedgeShares * SPY_Price',
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'SPY_Price'
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])
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##########
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##########
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##########
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# Set send_hedge_order to True to submit the order, not just generate it.
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# (Must also set read_only to False when creating the IbSessionTws instance.)
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send_hedge_order = False
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from ibapi.order import Order
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c.symbol = "SPY"
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rc = client.get_registered_contract(c)
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print(c)
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# Extract the hedge information from the hedge_shares table:
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hedge_info = hedge_shares.j_table.getRecord(0, 'HedgeShares', 'SPY_Price')
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hedge_qty = hedge_info[0]
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hedge_last_px = hedge_info[1]
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hedge_side = "BUY" if hedge_qty > 0 else "SELL"
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hedge_limit_px = hedge_last_px + 0.05 * (1 if hedge_side is "BUY" else -1)
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# Create an order with the IB API:
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order = Order()
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# order.account = "<account number>"
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order.action = hedge_side
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order.orderType = "LIMIT"
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order.totalQuantity = hedge_qty
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order.lmtPrice = hedge_limit_px
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order.eTradeOnly = False
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order.firmQuoteOnly = False
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print('Order: ' + str(order))
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if send_hedge_order:
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print('***** Sending order to ' + order.action + ' ' + str(
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order.totalQuantity) + ' shares of ' + c.symbol + '! *****')
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req = client.order_place(rc, order)
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else:
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print('Not actually sending order.')
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# To cancel orders:
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# req.cancel()
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# client.order_cancel_all()

‎src/deephaven_ib/__init__.py‎

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Original file line numberDiff line numberDiff line change
@@ -132,14 +132,24 @@ class BarDataType(Enum):
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"""Ask prices."""
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BID_ASK = 5
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"""Bid/Ask prices."""
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HISTORICAL_VOLATILITY = 6
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ADJUSTED_LAST = 6
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"""Bid/Ask prices."""
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HISTORICAL_VOLATILITY = 7
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"""Historical volatility."""
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OPTION_IMPLIED_VOLATILITY = 7
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OPTION_IMPLIED_VOLATILITY = 8
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"""Option implied volatility."""
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FEE_RATE = 8
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"""Fee rate."""
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REBATE_RATE = 9
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"""Rebate rate."""
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FEE_RATE = 10
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"""Fee rate."""
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YIELD_BID = 11
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"""Bid yield."""
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YIELD_ASK = 12
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"""Ask yield."""
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YIELD_BID_ASK = 13
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"""Bid/Ask yield."""
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YIELD_LAST = 14
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"""Last yield."""
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class BarSize(Enum):

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