Jhonathan Abreu
0a9dc2c71c
* Fix pandas converter to handle list of data with different symbols * Properly convert list of data into dataframe Take into consideration data for multiple symbols in the same list * Cleanup * Index dataframes by symbol object instead of SID string * Add symbol equality operator to compare against object * Exclude "ID" from option chain dataframe * Minor fix * Add greeks columns directly in option chain dataframe. Also add pass-through properties for greek values in OptionUniverse * Some cleanup * Minor fix * Add new QCAlgorithm.OptionChains() method - Use OptionChains as output - Add DataFrame to OptionChain and OptionChains - Rename Greeks classes - Add ISymbolProvider for classes that have a symbol (IBaseData, OptionContract) * Unify QCAlgorithmOptionChain API Also refactor OptionContract to handle: (1) Actual market data and option price model data, and (2) OptionUniverse data * Pass symbol properties to OptionUniverse option chain from algorithm * Format OptionContract for dataframe * Minor fix * Add multiple option chains api regression algorithms and other minor changes * Address peer review Add NullGreeks class: keep ModeledGreeks as internal as possible * Minor fix and add PandasConverter unit tests * Peer review: Non-thread-safe Lazy for Python * Handle Greeks unwrapping by PandasData * PandasData cleanup * Add data and other minor changes * Unit test fix * Update Pythonnet to 2.0.39 * Cleanup * PandasData handling children class members Address peer review * Fix: indexing symbol conversion in pandas mapper * Fix pandas mapper to convert string keys to symbol only when necessary * Cleanup * Cleanup * Add PandasColumn python class to handle proper indexing This allows propery hash and equality between Symbols, C# strings and Python strings * Minor fixes * Symbol cache improvements * Minor fix for cache miss * Revert PandasMapper reserved names and improvements * Minor fix * Revert reserved names * Minor fix for Symbol equality operators --------- Co-authored-by: Martin Molinero <martin.molinero1@gmail.com>
53 lines
2.4 KiB
Python
53 lines
2.4 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### Regression algorithm to test the OptionChainedUniverseSelectionModel class
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### </summary>
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class OptionChainedUniverseSelectionModelRegressionAlgorithm(QCAlgorithm):
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def initialize(self):
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self.universe_settings.resolution = Resolution.MINUTE
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self.set_start_date(2014, 6, 6)
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self.set_end_date(2014, 6, 6)
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self.set_cash(100000)
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universe = self.add_universe("my-minute-universe-name", lambda time: [ "AAPL", "TWX" ])
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self.add_universe_selection(
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OptionChainedUniverseSelectionModel(
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universe,
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lambda u: (u.strikes(-2, +2)
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# Expiration method accepts TimeSpan objects or integer for days.
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# The following statements yield the same filtering criteria
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.expiration(0, 180))
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)
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)
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def on_data(self, slice):
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if self.portfolio.invested or not (self.is_market_open("AAPL") and self.is_market_open("TWX")): return
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values = list(map(lambda x: x.value, filter(lambda x: x.key == "?AAPL" or x.key == "?TWX", slice.option_chains)))
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for chain in values:
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# we sort the contracts to find at the money (ATM) contract with farthest expiration
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contracts = sorted(sorted(sorted(chain, \
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key = lambda x: abs(chain.underlying.price - x.strike)), \
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key = lambda x: x.expiry, reverse=True), \
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key = lambda x: x.right, reverse=True)
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# if found, trade it
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if len(contracts) == 0: return
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symbol = contracts[0].symbol
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self.market_order(symbol, 1)
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self.market_on_close_order(symbol, -1)
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