pyqstrat package¶
Submodules¶
pyqstrat.pq_utils module¶
-
class
pyqstrat.pq_utils.
ReasonCode
[source]¶ Bases:
object
A class containing constants for predefined order reason codes. Prefer these predefined reason codes if they suit the reason you are creating your order. Otherwise, use your own string.
-
BACKTEST_END
= 'backtest end'¶
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ENTER_LONG
= 'enter long'¶
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ENTER_SHORT
= 'enter short'¶
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EXIT_LONG
= 'exit long'¶
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EXIT_SHORT
= 'exit short'¶
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MARKER_PROPERTIES
= {'backtest end': {'color': 'green', 'size': 50, 'symbol': '*'}, 'enter long': {'color': 'blue', 'size': 50, 'symbol': 'P'}, 'enter short': {'color': 'red', 'size': 50, 'symbol': 'P'}, 'exit long': {'color': 'blue', 'size': 50, 'symbol': 'X'}, 'exit short': {'color': 'red', 'size': 50, 'symbol': 'X'}, 'none': {'color': 'green', 'size': 50, 'symbol': 'o'}, 'roll future': {'color': 'green', 'size': 50, 'symbol': '>'}}¶
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NONE
= 'none'¶
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ROLL_FUTURE
= 'roll future'¶
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pyqstrat.pq_utils.
date_2_num
(d)[source]¶ Adopted from matplotlib.mdates.date2num so we don’t have to add a dependency on matplotlib here
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pyqstrat.pq_utils.
day_of_week_num
(a)[source]¶ From https://stackoverflow.com/questions/52398383/finding-day-of-the-week-for-a-datetime64 Get day of week for a numpy array of datetimes Monday is 0, Sunday is 6
- Parameters
a (numpy datetime64 or array of datetime64) –
- Returns
Monday is 0, Sunday is 6
- Return type
numpy int or numpy ndarray of int
>>> day_of_week_num(np.datetime64('2015-01-04')) 6
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pyqstrat.pq_utils.
decode_future_code
(future_code, as_str=True)[source]¶ Given a future code such as “X”, return either the month number (from 1 - 12) or the month abbreviation, such as “nov”
- Parameters
future_code (str) – the one letter future code
as_str (bool, optional) – If set, we return the abbreviation, if not, we return the month number
>>> decode_future_code('X', as_str = False) 11 >>> decode_future_code('X') 'nov'
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pyqstrat.pq_utils.
get_empty_np_value
(np_dtype)[source]¶ Get empty value for a given numpy datatype >>> a = np.array([‘2018-01-01’, ‘2018-01-03’], dtype = ‘M8[D]’) >>> get_empty_np_value(a.dtype) numpy.datetime64(‘NaT’)
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pyqstrat.pq_utils.
get_fut_code
(month)[source]¶ Given a month number such as 3 for March, return the future code for it, e.g. H >>> get_fut_code(3) ‘H’
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pyqstrat.pq_utils.
has_display
()[source]¶ If we are running in unit test mode or on a server, then don’t try to draw graphs, etc.
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pyqstrat.pq_utils.
infer_compression
(input_filename)[source]¶ Infers compression for a file from its suffix. For example, given “/tmp/hello.gz”, this will return “gzip” >>> infer_compression(“/tmp/hello.gz”) ‘gzip’ >>> infer_compression(“/tmp/abc.txt”) is None True
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pyqstrat.pq_utils.
infer_frequency
(timestamps)[source]¶ Returns most common frequency of date differences as a fraction of days :param timestamps: A numpy array of monotonically increasing datetime64
>>> timestamps = np.array(['2018-01-01 11:00:00', '2018-01-01 11:15:00', '2018-01-01 11:30:00', '2018-01-01 11:35:00'], dtype = 'M8[ns]') >>> print(round(infer_frequency(timestamps), 8)) 0.01041667
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pyqstrat.pq_utils.
is_newer
(filename, ref_filename)[source]¶ whether filename ctime (modfication time) is newer than ref_filename or either file does not exist >>> import time >>> import tempfile >>> temp_dir = tempfile.gettempdir() >>> touch(f’{temp_dir}/x.txt’) >>> time.sleep(0.1) >>> touch(f’{temp_dir}/y.txt’) >>> is_newer(f’{temp_dir}/y.txt’, f’{temp_dir}/x.txt’) True >>> touch(f’{temp_dir}/y.txt’) >>> time.sleep(0.1) >>> touch(f’{temp_dir}/x.txt’) >>> is_newer(f’{temp_dir}/y.txt’, f’{temp_dir}/x.txt’) False
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pyqstrat.pq_utils.
linear_interpolate
(a1, a2, x1, x2, x)[source]¶ >>> print(f'{linear_interpolate(3, 4, 8, 10, 8.9):.3f}') 3.450
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pyqstrat.pq_utils.
millis_since_epoch
(dt)[source]¶ Given a python datetime, return number of milliseconds between the unix epoch and the datetime. Returns a float since it can contain fractions of milliseconds as well >>> millis_since_epoch(datetime.datetime(2018, 1, 1)) 1514764800000.0
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pyqstrat.pq_utils.
monotonically_increasing
(array)[source]¶ Returns True if the array is monotonically_increasing, False otherwise
>>> monotonically_increasing(np.array(['2018-01-02', '2018-01-03'], dtype = 'M8[D]')) True >>> monotonically_increasing(np.array(['2018-01-02', '2018-01-02'], dtype = 'M8[D]')) False
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pyqstrat.pq_utils.
np_find_closest
(a, v)[source]¶ From https://stackoverflow.com/questions/8914491/finding-the-nearest-value-and-return-the-index-of-array-in-python Find index of closest value to array v in array a. Returns an array of the same size as v a must be sorted >>> assert(all(np_find_closest(np.array([3, 4, 6]), np.array([4, 2])) == np.array([1, 0])))
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pyqstrat.pq_utils.
np_get_index
(array, value)[source]¶ Get index of a value in a numpy array. Returns -1 if the value does not exist.
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pyqstrat.pq_utils.
np_rolling_window
(a, window)[source]¶ For applying rolling window functions to a numpy array See: https://stackoverflow.com/questions/6811183/rolling-window-for-1d-arrays-in-numpy >>> print(np.std(np_rolling_window(np.array([1, 2, 3, 4]), 2), 1)) [0.5 0.5 0.5]
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pyqstrat.pq_utils.
np_round
(a, clip)[source]¶ Round an array to the nearest clip
- Parameters
a (numpy numeric array) –
clip (float) – rounding value
>>> np_round(15.8, 0.25) 15.75
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pyqstrat.pq_utils.
percentile_of_score
(a)[source]¶ For each element in a, find the percentile of a its in. From stackoverflow.com/a/29989971/5351549 Like scipy.stats.percentileofscore but runs in O(n log(n)) time. >>> a = np.array([4, 3, 1, 2, 4.1]) >>> percentiles = percentile_of_score(a) >>> assert(all(np.isclose(np.array([ 75., 50., 0., 25., 100.]), percentiles)))
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pyqstrat.pq_utils.
resample_trade_bars
(df, sampling_frequency, resample_funcs=None)[source]¶ Downsample trade bars using sampling frequency
- Parameters
df (pd.DataFrame) – Must contain an index of numpy datetime64 type which is monotonically increasing
sampling_frequency (str) – See pandas frequency strings
(dict of str (resample_funcs) – int) : a dictionary of column name -> resampling function for any columns that are custom defined. Default None. If there is no entry for a custom column, defaults to ‘last’ for that column
- Returns
Resampled dataframe
- Return type
pd.DataFrame
>>> import math >>> df = pd.DataFrame({'date' : np.array(['2018-01-08 15:00:00', '2018-01-09 13:30:00', '2018-01-09 15:00:00', '2018-01-11 15:00:00'], dtype = 'M8[ns]'), ... 'o' : np.array([8.9, 9.1, 9.3, 8.6]), ... 'h' : np.array([9.0, 9.3, 9.4, 8.7]), ... 'l' : np.array([8.8, 9.0, 9.2, 8.4]), ... 'c' : np.array([8.95, 9.2, 9.35, 8.5]), ... 'v' : np.array([200, 100, 150, 300]), ... 'x' : np.array([300, 200, 100, 400]) ... }) >>> df['vwap'] = 0.5 * (df.l + df.h) >>> df.set_index('date', inplace = True) >>> df = resample_trade_bars(df, sampling_frequency = 'D', resample_funcs={'x' : lambda df, ... sampling_frequency : df.x.resample(sampling_frequency).agg(np.mean)}) >>> assert(len(df) == 4) >>> assert(math.isclose(df.vwap.iloc[1], 9.24)) >>> assert(np.isnan(df.vwap.iloc[2])) >>> assert(math.isclose(df.l[3], 8.4))
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pyqstrat.pq_utils.
resample_ts
(dates, values, sampling_frequency)[source]¶ Downsample a pair of dates and values using sampling frequency, using the last value if it does not exist at bin edge. See pandas.Series.resample
- Parameters
dates – a numpy datetime64 array
values – a numpy array
sampling_frequency – See pandas frequency strings
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pyqstrat.pq_utils.
resample_vwap
(df, sampling_frequency)[source]¶ Compute weighted average of vwap given higher frequency vwap and volume
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pyqstrat.pq_utils.
series_to_array
(series)[source]¶ Convert a pandas series to a numpy array. If the object is not a pandas Series return it back unchanged
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pyqstrat.pq_utils.
set_defaults
(df_float_sf=8, df_display_max_rows=200, df_display_max_columns=99, np_seterr='raise', plot_style='ggplot', mpl_figsize=(8, 6))[source]¶ Set some display defaults to make it easier to view dataframes and graphs.
- Parameters
df_float_sf – Number of significant figures to show in dataframes (default 4). Set to None to use pandas defaults
df_display_max_rows – Number of rows to display for pandas dataframes when you print them (default 200). Set to None to use pandas defaults
df_display_max_columns – Number of columns to display for pandas dataframes when you print them (default 99). Set to None to use pandas defaults
np_seterr – Error mode for numpy warnings. See numpy seterr function for details. Set to None to use numpy defaults
plot_style – Style for matplotlib plots. Set to None to use default plot style.
mpl_figsize – Default figure size to use when displaying matplotlib plots (default 8,6). Set to None to use defaults
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pyqstrat.pq_utils.
shift_np
(array, n, fill_value=None)[source]¶ Similar to pandas.Series.shift but works on numpy arrays.
- Parameters
array – The numpy array to shift
n – Number of places to shift, can be positive or negative
fill_value – After shifting, there will be empty slots left in the array. If set, fill these with fill_value. If fill_value is set to None (default), we will fill these with False for boolean arrays, np.nan for floats
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pyqstrat.pq_utils.
str2date
(s)[source]¶ Converts a string like “2008-01-15 15:00:00” to a numpy datetime64. If s is not a string, return s back
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pyqstrat.pq_utils.
strtup2date
(tup)[source]¶ Converts a string tuple like (“2008-01-15”, “2009-01-16”) to a numpy datetime64 tuple. If the tuple does not contain strings, return it back unchanged
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pyqstrat.pq_utils.
to_csv
(df, file_name, index=False, compress=False, *args, **kwargs)[source]¶ Creates a temporary file then renames to the permanent file so we don’t have half written files. Also optionally compresses using the xz algorithm
pyqstrat.pq_types module¶
-
class
pyqstrat.pq_types.
Contract
[source]¶ Bases:
object
-
static
clear
()[source]¶ When running Python interactively you may create a Contract with a given symbol multiple times because you don’t restart Python therefore global variables are not cleared. This function clears global Contracts
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static
create
(symbol, contract_group, expiry=None, multiplier=1.0, properties=None)[source]¶ - Parameters
symbol (str) – A unique string reprenting this contract. e.g IBM or ESH9
contract_group (
ContractGroup
) – We sometimes need to group contracts for calculating PNL, for example, you may have a strategy which has 3 legs, a long option, a short option and a future or equity used to hedge delta. In this case, you will be trading different symbols over time as options and futures expire, but you may want to track PNL for each leg using a contract group for each leg. So you could create contract groups ‘Long Option’, ‘Short Option’ and ‘Hedge’ and assign contracts to these.(obj (properties) – np.datetime64 or
datetime.datetime
, optional): In the case of a future or option, the date and time when the contract expires. For equities and other non expiring contracts, set this to None. Default None.multiplier (float, optional) – If the market price convention is per unit, and the unit is not the same as contract size, set the multiplier here. For example, for E-mini contracts, each contract is 50 units and the price is per unit, so multiplier would be 50. Default 1
(obj – types.SimpleNamespace, optional): Any data you want to store with this contract. For example, you may want to store option strike. Default None
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static
-
class
pyqstrat.pq_types.
ContractGroup
[source]¶ Bases:
object
A way to group contracts for figuring out which indicators, rules and signals to apply to a contract and for PNL reporting
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class
pyqstrat.pq_types.
OrderStatus
[source]¶ Bases:
object
Enum for order status
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FILLED
= 'filled'¶
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OPEN
= 'open'¶
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class
pyqstrat.pq_types.
Trade
(contract, timestamp, qty, price, fee=0.0, commission=0.0, order=None, properties=None)[source]¶ Bases:
object
-
__init__
(contract, timestamp, qty, price, fee=0.0, commission=0.0, order=None, properties=None)[source]¶ - Parameters
contract (
Contract
) –timestamp (
np.datetime64
) – Trade execution datetimeqty (float) – Number of contracts or shares filled
price (float) – Trade price
fee (float, optional) – Fees paid to brokers or others. Default 0
commision (float, optional) – Commission paid to brokers or others. Default 0
order (
pq.Order
, optional) – A reference to the order that created this trade. Default None(obj (properties) – types.SimpleNamespace, optional): Any data you want to store with this contract. For example, you may want to store bid / ask prices at time of trade. Default None
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pyqstrat.holiday_calendars module¶
-
class
pyqstrat.holiday_calendars.
Calendar
(holidays)[source]¶ Bases:
object
-
EUREX
= 'eurex'¶
-
NYSE
= 'nyse'¶
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__init__
(holidays)[source]¶ Do not use this function directly. Use Calendar.get_calendar instead :param holidays: holidays for this calendar, excluding weekends :type holidays: np.array of datetime64[D]
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add_calendar
(holidays)[source]¶ Add a trading calendar to the class level calendars dict
- Parameters
exchange_name (str) – Name of the exchange.
holidays (np.array of datetime64[D]) – holidays for this exchange, excluding weekends
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add_trading_days
(start, num_days, roll='raise')[source]¶ Adds trading days to a start date
- Parameters
start – np.datetime64 or str or datetime
num_days (int) – number of trading days to add
roll (str, optional) – one of ‘raise’, ‘nat’, ‘forward’, ‘following’, ‘backward’, ‘preceding’, ‘modifiedfollowing’, ‘modifiedpreceding’ or ‘allow’} ‘allow’ is a special case in which case, adding 1 day to a holiday will act as if it was not a holiday, and give you the next business day’ The rest of the values are the same as in the numpy busday_offset function From numpy documentation: How to treat dates that do not fall on a valid day. The default is ‘raise’. ‘raise’ means to raise an exception for an invalid day. ‘nat’ means to return a NaT (not-a-time) for an invalid day. ‘forward’ and ‘following’ mean to take the first valid day later in time. ‘backward’ and ‘preceding’ mean to take the first valid day earlier in time. ‘modifiedfollowing’ means to take the first valid day later in time unless it is across a Month boundary, in which case to take the first valid day earlier in time. ‘modifiedpreceding’ means to take the first valid day earlier in time unless it is across a Month boundary, in which case to take the first valid day later in time.
- Returns
The date num_days trading days after start
- Return type
np.datetime64[D]
>>> calendar = Calendar.get_calendar(Calendar.NYSE) >>> calendar.add_trading_days(datetime.date(2015, 12, 24), 1) numpy.datetime64('2015-12-28') >>> calendar.add_trading_days(np.datetime64('2017-04-15'), 0, roll = 'preceding') # 4/14/2017 is a Friday and a holiday numpy.datetime64('2017-04-13') >>> calendar.add_trading_days(np.datetime64('2017-04-08'), 0, roll = 'preceding') # 4/7/2017 is a Friday and not a holiday numpy.datetime64('2017-04-07') >>> calendar.add_trading_days(np.datetime64('2019-02-17 15:25'), 1, roll = 'allow') numpy.datetime64('2019-02-19T15:25') >>> calendar.add_trading_days(np.datetime64('2019-02-17 15:25'), -1, roll = 'allow') numpy.datetime64('2019-02-15T15:25')
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get_calendar
()[source]¶ Get a calendar object for the given exchange:
- Parameters
exchange_name (str) – The exchange for which you want a calendar. Calendar.NYSE, Calendar.EUREX are predefined.
you want to add a new calendar, use the add_calendar class level function (If) –
- Returns
The calendar object
- Return type
-
get_trading_days
(start, end, include_first=False, include_last=True)[source]¶ Get back a list of numpy dates that are trading days between the start and end
>>> nyse = Calendar.get_calendar(Calendar.NYSE) >>> nyse.get_trading_days('2005-01-01', '2005-01-08') array(['2005-01-03', '2005-01-04', '2005-01-05', '2005-01-06', '2005-01-07'], dtype='datetime64[D]') >>> nyse.get_trading_days(datetime.date(2005, 1, 1), datetime.date(2005, 2, 1)) array(['2005-01-03', '2005-01-04', '2005-01-05', '2005-01-06', '2005-01-07', '2005-01-10', '2005-01-11', '2005-01-12', '2005-01-13', '2005-01-14', '2005-01-18', '2005-01-19', '2005-01-20', '2005-01-21', '2005-01-24', '2005-01-25', '2005-01-26', '2005-01-27', '2005-01-28', '2005-01-31', '2005-02-01'], dtype='datetime64[D]') >>> nyse.get_trading_days(datetime.date(2016, 1, 5), datetime.date(2016, 1, 29), include_last = False) array(['2016-01-06', '2016-01-07', '2016-01-08', '2016-01-11', '2016-01-12', '2016-01-13', '2016-01-14', '2016-01-15', '2016-01-19', '2016-01-20', '2016-01-21', '2016-01-22', '2016-01-25', '2016-01-26', '2016-01-27', '2016-01-28'], dtype='datetime64[D]') >>> nyse.get_trading_days('2017-07-04', '2017-07-08', include_first = False) array(['2017-07-05', '2017-07-06', '2017-07-07'], dtype='datetime64[D]') >>> nyse.get_trading_days(np.datetime64('2017-07-04'), np.datetime64('2017-07-08'), include_first = False) array(['2017-07-05', '2017-07-06', '2017-07-07'], dtype='datetime64[D]')
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is_trading_day
(dates)[source]¶ Returns whether the date is not a holiday or a weekend
- Parameters
dates – str or datetime.datetime or np.datetime64[D] or numpy array of np.datetime64[D]
- Returns
Whether this date is a trading day
- Return type
bool
>>> import datetime >>> eurex = Calendar.get_calendar(Calendar.EUREX) >>> eurex.is_trading_day('2016-12-25') False >>> eurex.is_trading_day(datetime.date(2016, 12, 22)) True >>> nyse = Calendar.get_calendar(Calendar.NYSE) >>> nyse.is_trading_day('2017-04-01') # Weekend False >>> nyse.is_trading_day(np.arange('2017-04-01', '2017-04-09', dtype = np.datetime64)) # doctest:+ELLIPSIS array([False, False, True, True, True, True, True, False]...)
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num_trading_days
(start, end, include_first=False, include_last=True)[source]¶ Count the number of trading days between two date series including those two dates You can pass in a string like ‘2009-01-01’ or a python date or a pandas series for start and end
>>> eurex = Calendar.get_calendar(Calendar.EUREX) >>> eurex.num_trading_days('2009-01-01', '2011-12-31') 772 >>> dates = pd.date_range('20130101',periods=8) >>> increments = np.array([5, 0, 3, 9, 4, 10, 15, 29]) >>> import warnings >>> import pandas as pd >>> warnings.filterwarnings(action = 'ignore', category = pd.errors.PerformanceWarning) >>> dates2 = dates + increments * dates.freq >>> df = pd.DataFrame({'x': dates, 'y' : dates2}) >>> df.iloc[4]['x'] = np.nan >>> df.iloc[6]['y'] = np.nan >>> nyse = Calendar.get_calendar(Calendar.NYSE) >>> np.set_printoptions(formatter = {'float' : lambda x : f'{x:.1f}'}) # After numpy 1.13 positive floats don't have a leading space for sign >>> print(nyse.num_trading_days(df.x, df.y)) [3.0 0.0 1.0 5.0 nan 8.0 nan 20.0]
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pyqstrat.account module¶
-
class
pyqstrat.account.
Account
(contract_groups, timestamps, price_function, strategy_context, starting_equity=1000000.0, pnl_calc_time=900)[source]¶ Bases:
object
An Account calculates pnl for a set of contracts
-
__init__
(contract_groups, timestamps, price_function, strategy_context, starting_equity=1000000.0, pnl_calc_time=900)[source]¶ - Parameters
contract_groups (list of
ContractGroup
) – Contract groups that we want to compute PNL fortimestamps (list of np.datetime64) – Timestamps that we might compute PNL at
price_function (function) – Function that takes a symbol, timestamps, index, strategy context and returns the price used to compute pnl
starting_equity (float, optional) – Starting equity in account currency. Default 1.e6
pnl_calc_time (int, optional) – Number of minutes past midnight that we should calculate PNL at. Default 15 * 60, i.e. 3 pm
-
calc
(timestamp)[source]¶ Computes P&L and stores it internally for all contracts.
- Parameters
timestamp (np.datetime64) – timestamp to compute P&L at. Account remembers the last timestamp it computed P&L up to and will compute P&L between these and including timestamp. If there is more than one day between the last index and current index, we will include pnl for at the defined pnl_calc_time for those dates as well.
-
df_account_pnl
(contract_group=None)[source]¶ Returns PNL at the account level.
- Parameters
contract_group (
ContractGroup
, optional) – If set, we only return pnl for this contract_group
-
df_pnl
(contract_groups=None)[source]¶ Returns a dataframe with P&L columns broken down by contract group and symbol
- Parameters
contract_group (
ContractGroup
, optional) – Return PNL for this contract group. If None (default), include all contract groups
-
df_trades
(contract_group=None, start_date=None, end_date=None)[source]¶ Returns a dataframe of trades
- Parameters
contract_group (
ContractGroup
, optional) – Return trades for this contract group. If None (default), include all contract groupsstart_date (
np.datetime64
, optional) – Include trades with date greater than or equal to this timestamp.end_date (
np.datetime64
, optional) – Include trades with date less than or equal to this timestamp.
-
equity
(timestamp)[source]¶ Returns equity in this account in Account currency. Will cause calculation if Account has not previously calculated up to this date
-
-
class
pyqstrat.account.
ContractPNL
(contract, account_timestamps, price_function, strategy_context)[source]¶ Bases:
object
Computes pnl for a single contract over time given trades and market data
-
pyqstrat.account.
calc_trade_pnl
(open_qtys, open_prices, new_qtys, new_prices, multiplier)[source]¶ >>> print(calc_trade_pnl( ... open_qtys = np.array([], dtype = np.float), open_prices = np.array([], dtype = np.float), ... new_qtys = np.array([-8, 9, -4]), new_prices = np.array([10, 11, 6]), multiplier = 100)) (array([-3.]), array([6.]), -3.0, 6.0, -1300.0) >>> print(calc_trade_pnl(open_qtys = np.array([], dtype = np.float), open_prices = np.array([], dtype = np.float), new_qtys = np.array([3, 10, -5]), ... new_prices = np.array([51, 50, 45]), multiplier = 100)) (array([8.]), array([50.]), 8.0, 50.0, -2800.0) >>> print(calc_trade_pnl(open_qtys = np.array([]), open_prices = np.array([]), ... new_qtys = np.array([-58, -5, -5, 6, -8, 5, 5, -5, 19, 7, 5, -5, 39]), ... new_prices = np.array([2080, 2075.25, 2070.75, 2076, 2066.75, 2069.25, 2074.75, 2069.75, 2087.25, 2097.25, 2106, 2088.25, 2085.25]), ... multiplier = 50)) (array([], dtype=float64), array([], dtype=float64), 0.0, 0, -33762.5)
pyqstrat.orders module¶
-
class
pyqstrat.orders.
LimitOrder
(contract, timestamp, qty, limit_price, reason_code='none', properties=None, status='open')[source]¶ Bases:
object
-
__init__
(contract, timestamp, qty, limit_price, reason_code='none', properties=None, status='open')[source]¶ - Parameters
contract (
Contract
) –timestamp (
np.datetime64
) – Time the order was placedqty (float) – Number of contracts or shares. Use a negative quantity for sell orders
limit_price (float) – Limit price (float)
reason_code (str, optional) – The reason this order was created. Prefer a predefined constant from the ReasonCode class if it matches your reason for creating this order. Default None
properties (
SimpleNamespace
, optional) – Any order specific data we want to store. Default Nonestatus (str, optional) – Status of the order, “open”, “filled”, etc. (default “open”)
-
-
class
pyqstrat.orders.
MarketOrder
(contract, timestamp, qty, reason_code='none', properties=None, status='open')[source]¶ Bases:
object
-
__init__
(contract, timestamp, qty, reason_code='none', properties=None, status='open')[source]¶ - Parameters
contract (
Contract
) –timestamp (
np.datetime64
) – Time the order was placedqty (float) – Number of contracts or shares. Use a negative quantity for sell orders
reason_code (str, optional) – The reason this order was created. Prefer a predefined constant from the ReasonCode class if it matches your reason for creating this order. Default None
properties (
SimpleNamespace
, optional) – Any order specific data we want to store. Default Nonestatus (str, optional) – Status of the order, “open”, “filled”, etc. (default “open”)
-
-
class
pyqstrat.orders.
RollOrder
(contract, timestamp, close_qty, reopen_qty, reason_code='roll future', properties=None, status='open')[source]¶ Bases:
object
A roll order is used to roll a future from one series to the next. It represents a sell of one future and the buying of another future.
-
__init__
(contract, timestamp, close_qty, reopen_qty, reason_code='roll future', properties=None, status='open')[source]¶ - Parameters
contract (
Contract
) –timestamp (
np.datetime64
) – Time the order was placedclose_qty (float) – Quantity of the future you are rolling
reopen_qty (float) – Quantity of the future you are rolling to
reason_code (str, optional) – The reason this order was created. Prefer a predefined constant from the ReasonCode class if it matches your reason for creating this order. Default None
properties (
SimpleNamespace
, optional) – Any order specific data we want to store. Default Nonestatus (str, optional) – Status of the order, “open”, “filled”, etc. (default “open”)
-
-
class
pyqstrat.orders.
StopLimitOrder
(contract, timestamp, qty, trigger_price, limit_price=nan, reason_code='none', properties=None, status='open')[source]¶ Bases:
object
Used for stop loss or stop limit orders. The order is triggered when price goes above or below trigger price, depending on whether this is a short or long order. Becomes either a market or limit order at that point, depending on whether you set the limit price or not.
-
__init__
(contract, timestamp, qty, trigger_price, limit_price=nan, reason_code='none', properties=None, status='open')[source]¶ - Parameters
contract (
Contract
) –timestamp (
np.datetime64
) – Time the order was placedqty (float) – Number of contracts or shares. Use a negative quantity for sell orders
trigger_price (float) – Order becomes a market or limit order if price crosses trigger_price.
limit_price (float, optional) – If not set (default), order becomes a market order when price crosses trigger price. Otherwise it becomes a limit order. Default np.nan
reason_code (str, optional) – The reason this order was created. Prefer a predefined constant from the ReasonCode class if it matches your reason for creating this order. Default None
properties (
SimpleNamespace
, optional) – Any order specific data we want to store. Default Nonestatus (str, optional) – Status of the order, “open”, “filled”, etc. (default “open”)
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pyqstrat.strategy module¶
-
class
pyqstrat.strategy.
Strategy
(timestamps, contract_groups, price_function, starting_equity=1000000.0, pnl_calc_time=901, trade_lag=0, run_final_calc=True, strategy_context=None)[source]¶ Bases:
object
-
__init__
(timestamps, contract_groups, price_function, starting_equity=1000000.0, pnl_calc_time=901, trade_lag=0, run_final_calc=True, strategy_context=None)[source]¶ - Parameters
timestamps (np.array of np.datetime64) – The “heartbeat” of the strategy. We will evaluate trading rules and simulate the market at these times.
price_function – A function that returns the price of a contract at a given timestamp
contract_groups (list of
ContractGroup
) – The contract groups we will potentially trade.starting_equity (float, optional) – Starting equity in Strategy currency. Default 1.e6
pnl_calc_time (int, optional) – Time of day used to calculate PNL. Default 15 * 60 (3 pm)
trade_lag (int, optional) – Number of bars you want between the order and the trade. For example, if you think it will take 5 seconds to place your order in the market, and your bar size is 1 second, set this to 5. Set this to 0 if you want to execute your trade at the same time as you place the order, for example, if you have daily bars. Default 0.
run_final_calc (bool, optional) – If set, calculates unrealized pnl and net pnl as well as realized pnl when strategy is done. If you don’t need unrealized pnl, turn this off for faster run time. Default True
strategy_context (
types.SimpleNamespace
, optional) – A storage class where you can store key / value pairs relevant to this strategy. For example, you may have a pre-computed table of correlations that you use in the indicator or trade rule functions. If not set, the __init__ function will create an empty member strategy_context object that you can access.
-
add_indicator
(name, indicator, contract_groups=None, depends_on=None)[source]¶ - Parameters
name – Name of the indicator
indicator – A function that takes strategy timestamps and other indicators and returns a numpy array containing indicator values. The return array must have the same length as the timestamps object. Can also be a numpy array or a pandas Series in which case we just store the values.
contract_groups (list of
ContractGroup
, optional) – Contract groups that this indicator applies to. If not set, it applies to all contract groups. Default None.depends_on (list of str, optional) – Names of other indicators that we need to compute this indicator. Default None.
-
add_market_sim
(market_sim_function)[source]¶ Add a market simulator. A market simulator takes a list of Orders as input and returns a list of Trade objects.
- Parameters
market_sim_function (function) – A function that takes a list of Orders and Indicators as input and returns a list of Trade objects
-
add_rule
(name, rule_function, signal_name, sig_true_values=None, position_filter=None)[source]¶ - Add a trading rule. Trading rules are guaranteed to run in the order in which you add them. For example, if you set trade_lag to 0,
and want to exit positions and re-enter new ones in the same bar, make sure you add the exit rule before you add the entry rule to the strategy.
- Parameters
name (str) – Name of the trading rule
rule_function (function) – A trading rule function that returns a list of Orders
signal_name (str) – The strategy will call the trading rule function when the signal with this name matches sig_true_values
sig_true_values (numpy array, optional) – If the signal value at a bar is equal to one of these values, the Strategy will call the trading rule function. Default [TRUE]
position_filter (str, optional) – Can be “zero”, “nonzero” or None. Zero rules are only triggered when the corresponding contract positions are 0 Nonzero rules are only triggered when the corresponding contract positions are non-zero. If not set, we don’t look at position before triggering the rule. Default None
-
add_signal
(name, signal_function, contract_groups=None, depends_on_indicators=None, depends_on_signals=None)[source]¶ - Parameters
name (str) – Name of the signal
signal_function (function) – A function that takes timestamps and a dictionary of indicator value arrays and returns a numpy array containing signal values. The return array must have the same length as the input timestamps
contract_groups (list of
ContractGroup
, optional) – Contract groups that this signal applies to. If not set, it applies to all contract groups. Default None.depends_on_indicators (list of str, optional) – Names of indicators that we need to compute this signal. Default None.
depends_on_signals (list of str, optional) – Names of other signals that we need to compute this signal. Default None.
-
df_data
(contract_groups=None, add_pnl=True, start_date=None, end_date=None)[source]¶ Add indicators and signals to end of market data and return as a pandas dataframe.
- Parameters
contract_groups (list of
ContractGroup
, optional) – list of contract groups to include. All if set to None (default)add_pnl – If True (default), include P&L columns in dataframe
start_date – string or numpy datetime64. Default None
end_date – string or numpy datetime64: Default None
-
df_orders
(contract_group=None, start_date=None, end_date=None)[source]¶ Returns a dataframe with data from orders with the given contract group and with order date between (and including) start date and end date if they are specified. If contract_group is None orders for all contract_groups are returned
-
df_pnl
(contract_group=None)[source]¶ Returns a dataframe with P&L columns. If contract group is set to None (default), sums up P&L across all contract groups
-
df_returns
(contract_group=None, sampling_frequency='D')[source]¶ Return a dataframe of returns and equity indexed by date.
- Parameters
contract_group (
ContractGroup
, optional) – The contract group to get returns for. If set to None (default), we return the sum of PNL for all contract groupssampling_frequency – Downsampling frequency. Default is None. See pandas frequency strings for possible values
-
df_trades
(contract_group=None, start_date=None, end_date=None)[source]¶ Returns a dataframe with data from trades with the given contract group and with trade date between (and including) start date and end date if they are specified. If contract_group is None trades for all contract_groups are returned
-
evaluate_returns
(contract_group=None, plot=True, display_summary=True, float_precision=4, return_metrics=False)[source]¶ Returns a dictionary of common return metrics.
- Parameters
contract_group (
ContractGroup
, optional) – Contract group to evaluate or None (default) for all contract groupsplot (bool) – If set to True, display plots of equity, drawdowns and returns. Default False
float_precision (float, optional) – Number of significant figures to show in returns. Default 4
return_metrics (bool, optional) – If set, we return the computed metrics as a dictionary
-
orders
(contract_group=None, start_date=None, end_date=None)[source]¶ Returns a list of orders with the given contract group and with order date between (and including) start date and end date if they are specified. If contract_group is None orders for all contract_groups are returned
-
plot
(contract_groups=None, primary_indicators=None, primary_indicators_dual_axis=None, secondary_indicators=None, secondary_indicators_dual_axis=None, indicator_properties=None, signals=None, signal_properties=None, pnl_columns=None, title=None, figsize=(20, 15), date_range=None, date_format=None, sampling_frequency=None, trade_marker_properties=None, hspace=0.15)[source]¶ Plot indicators, signals, trades, position, pnl
- Parameters
contract_groups (list of
ContractGroup
, optional) – Contract groups to plot or None (default) for all contract groups.indicators (primary) – List of indicators to plot in the main indicator section. Default None (plot everything)
indicators – List of indicators to plot in the secondary indicator section. Default None (don’t plot anything)
(dict of str (trade_marker_properties) – dict, optional): If set, we use the line color, line type indicated for the given indicators
signals (list of str, optional) – Signals to plot. Default None (plot everything).
plot_equity (bool, optional) – If set, we plot the equity curve. Default is True
title (list of str, optional) – Title of plot. Default None
figsize (tuple of int) – Figure size. Default (20, 15)
date_range (tuple of str or np.datetime64, optional) – Used to restrict the date range of the graph. Default None
date_format (str, optional) – Date format for tick labels on x axis. If set to None (default), will be selected based on date range. See matplotlib date format strings
sampling_frequency (str, optional) – Downsampling frequency. The graph may get too busy if you have too many bars of data, in which case you may want to downsample before plotting. See pandas frequency strings for possible values. Default None.
(dict of str – tuple, optional): A dictionary of order reason code -> marker shape, marker size, marker color for plotting trades with different reason codes. Default is None in which case the dictionary from the ReasonCode class is used
hspace (float, optional) – Height (vertical) space between subplots. Default is 0.15
-
plot_returns
(contract_group=None)[source]¶ Display plots of equity, drawdowns and returns for the given contract group or for all contract groups if contract_group is None (default)
-
run_indicators
(indicator_names=None, contract_groups=None, clear_all=False)[source]¶ Calculate values of the indicators specified and store them.
- Parameters
indicator_names (list of str, optional) – List of indicator names. If None (default) run all indicators
contract_groups (list of
ContractGroup
, optional) – Contract group to run this indicator for. If None (default), we run it for all contract groups.clear_all (bool, optional) – If set, clears all indicator values before running. Default False.
-
run_rules
(rule_names=None, contract_groups=None, start_date=None, end_date=None)[source]¶ Run trading rules.
- Parameters
rule_names – List of rule names. If None (default) run all rules
contract_groups (list of
ContractGroup
, optional) – Contract groups to run this rule for. If None (default), we run it for all contract groups.start_date – Run rules starting from this date. Default None
end_date – Don’t run rules after this date. Default None
-
run_signals
(signal_names=None, contract_groups=None, clear_all=False)[source]¶ Calculate values of the signals specified and store them.
- Parameters
signal_names (list of str, optional) – List of signal names. If None (default) run all signals
contract_groups (list of
ContractGroup
, optional) – Contract groups to run this signal for. If None (default), we run it for all contract groups.clear_all (bool, optional) – If set, clears all signal values before running. Default False.
-
pyqstrat.portfolio module¶
-
class
pyqstrat.portfolio.
Portfolio
(name='main')[source]¶ Bases:
object
A portfolio contains one or more strategies that run concurrently so you can test running strategies that are uncorrelated together.
-
add_strategy
(name, strategy)[source]¶ - Parameters
name – Name of the strategy
strategy – Strategy object
-
df_returns
(sampling_frequency='D', strategy_names=None)[source]¶ Return dataframe containing equity and returns with a date index. Equity and returns are combined from all strategies passed in.
- Parameters
sampling_frequency – Date frequency for rows. Default ‘D’ for daily so we will have one row per day
strategy_names – A list of strategy names. By default this is set to None and we use all strategies.
-
evaluate_returns
(sampling_frequency='D', strategy_names=None, plot=True, float_precision=4)[source]¶ Returns a dictionary of common return metrics.
- Parameters
sampling_frequency – Date frequency. Default ‘D’ for daily so we downsample to daily returns before computing metrics
strategy_names – A list of strategy names. By default this is set to None and we use all strategies.
plot – If set to True, display plots of equity, drawdowns and returns. Default False
float_precision – Number of significant figures to show in returns. Default 4
-
plot
(sampling_frequency='D', strategy_names=None)[source]¶ Display plots of equity, drawdowns and returns
- Parameters
sampling_frequency – Date frequency. Default ‘D’ for daily so we downsample to daily returns before computing metrics
strategy_names – A list of strategy names. By default this is set to None and we use all strategies.
-
run
(strategy_names=None, start_date=None, end_date=None)[source]¶ Run indicators, signals and rules.
- Parameters
strategy_names – A list of strategy names. By default this is set to None and we use all strategies.
start_date – Run rules starting from this date. Sometimes we have a few strategies in a portfolio that need different lead times before they are ready to trade so you can set this so they are all ready by this date. Default None
end_date – Don’t run rules after this date. Default None
-
run_indicators
(strategy_names=None)[source]¶ Compute indicators for the strategies specified
- Parameters
strategy_names – A list of strategy names. By default this is set to None and we use all strategies.
-
pyqstrat.optimize module¶
-
class
pyqstrat.optimize.
Experiment
(suggestion, cost, other_costs)[source]¶ Bases:
object
An Experiment stores a suggestion and its result
-
suggestion
¶ A dictionary of variable name -> value
-
cost
¶ A float representing output of the function we are testing with this suggestion as input.
-
other_costs
¶ A dictionary of other results we want to store and look at later.
-
-
class
pyqstrat.optimize.
Optimizer
(name, generator, cost_func, max_processes=None)[source]¶ Bases:
object
Optimizer is used to optimize parameters for a strategy.
-
__init__
(name, generator, cost_func, max_processes=None)[source]¶ - Parameters
name – string used to display title in plotting, etc.
generator – A generator (see Python Generators) that takes no inputs and yields a list of dictionaries with parameter name -> parameter value.
cost_func – A function that takes a dictionary of parameter name -> parameter value as input and outputs cost for that set of parameters.
max_processes – If not set, the Optimizer will look at the number of CPU cores on your machine to figure out how many processes to run.
-
df_experiments
(sort_column='cost', ascending=True)[source]¶ Returns a dataframe containing experiment data, sorted by sort_column (default “cost”)
-
experiment_list
(sort_order='lowest_cost')[source]¶ Returns the list of experiments we have run
- Parameters
sort_order – Can be set to lowest_cost, highest_cost or sequence. If set to sequence, experiments are returned in the sequence in which they were run
-
plot_2d
(x, y='all', plot_type='line', figsize=(15, 8), marker='X', marker_size=50, marker_color='r', xlim=None, hspace=None)[source]¶ Creates a 2D plot of the optimization output for plotting 1 parameter and costs.
- Parameters
x – Name of the parameter to plot on the x axis, corresponding to the same name in the generator.
y – Can be one of: “cost” The name of another cost variable corresponding to the output from the cost function “all”, which creates a subplot for cost plus all other costs
plot_type – line or scatter (default line)
figsize – Figure size
marker – Adds a marker to each point in x, y to show the actual data used for interpolation. You can set this to None to turn markers off.
hspace – Vertical space between subplots
-
plot_3d
(x, y, z='all', plot_type='surface', figsize=(15, 15), interpolation='linear', cmap='viridis', marker='X', marker_size=50, marker_color='r', xlim=None, ylim=None, hspace=None)[source]¶ Creates a 3D plot of the optimization output for plotting 2 parameters and costs.
- Parameters
x – Name of the parameter to plot on the x axis, corresponding to the same name in the generator.
y – Name of the parameter to plot on the y axis, corresponding to the same name in the generator.
z – Can be one of: “cost” The name of another cost variable corresponding to the output from the cost function “all”, which creates a subplot for cost plus all other costs
plot_type – surface or contour (default surface)
figsize – Figure size
interpolation – Can be ‘linear’, ‘nearest’ or ‘cubic’ for plotting z points between the ones passed in. See scipy.interpolate.griddata for details
cmap – Colormap to use (default viridis). See matplotlib colormap for details
marker – Adds a marker to each point in x, y, z to show the actual data used for interpolation. You can set this to None to turn markers off.
hspace – Vertical space between subplots
-
pyqstrat.plot module¶
-
class
pyqstrat.plot.
BucketedValues
(name, bucket_names, bucket_values, proportional_widths=True, show_means=True, show_all=True, show_outliers=False, notched=False)[source]¶ Bases:
object
Data in a subplot where x axis is a categorical we summarize properties of a numpy array. For example, drawing a boxplot with percentiles.
-
__init__
(name, bucket_names, bucket_values, proportional_widths=True, show_means=True, show_all=True, show_outliers=False, notched=False)[source]¶ - Parameters
name – name used for this data in a plot legend
bucket_names – list of strings used on x axis labels
bucket_values – list of numpy arrays that are summarized in this plot
proportional_widths – if set to True, the width each box in the boxplot will be proportional to the number of items in its corresponding array
show_means – Whether to display a marker where the mean is for each array
show_outliers – Whether to show markers for outliers that are outside the whiskers. Box is at Q1 = 25%, Q3 = 75% quantiles, whiskers are at Q1 - 1.5 * (Q3 - Q1), Q3 + 1.5 * (Q3 - Q1)
notched – Whether to show notches indicating the confidence interval around the median
-
-
class
pyqstrat.plot.
DateFormatter
(timestamps, fmt)[source]¶ Bases:
matplotlib.ticker.Formatter
Formats timestamps on plot axes. See matplotlib Formatter
-
class
pyqstrat.plot.
DateLine
(date, name=None, line_type='dashed', color=None)[source]¶ Bases:
object
Draw a vertical line on a plot with a datetime x-axis
-
class
pyqstrat.plot.
HorizontalLine
(y, name=None, line_type='dashed', color=None)[source]¶ Bases:
object
Draws a horizontal line on a subplot
-
class
pyqstrat.plot.
Plot
(subplot_list, title=None, figsize=(15, 8), date_range=None, date_format=None, sampling_frequency=None, show_grid=True, show_date_gaps=True, hspace=0.15)[source]¶ Bases:
object
Top level plot containing a list of subplots to draw
-
__init__
(subplot_list, title=None, figsize=(15, 8), date_range=None, date_format=None, sampling_frequency=None, show_grid=True, show_date_gaps=True, hspace=0.15)[source]¶ - Parameters
subplot_list – List of Subplot objects to draw
title – Title for this plot. Default None
figsize – Figure size. Default (15, 8)
date_range – Tuple of strings or numpy datetime64 limiting timestamps to draw. e.g. (“2018-01-01 14:00”, “2018-01-05”). Default None
date_format – Date format to use for x-axis
sampling_frequency – Set this to downsample subplots that have a datetime x axis. For example, if you have minute bar data, you might want to subsample to hours if the plot is too crowded. See pandas time frequency strings for possible values. Default None
show_grid – If set to True, show a grid on the subplots. Default True
show_date_gaps – If set to True, then when there is a gap between timestamps will draw a dashed vertical line. For example, you may have minute bars and a gap between end of trading day and beginning of next day. Even if set to True, this will turn itself off if there are too many gaps to avoid clutter. Default True
hspace – Height (vertical) space between subplots. Default 0.15
-
-
class
pyqstrat.plot.
Subplot
(data_list, secondary_y=None, title=None, xlabel=None, ylabel=None, zlabel=None, date_lines=None, horizontal_lines=None, vertical_lines=None, xlim=None, ylim=None, height_ratio=1.0, display_legend=True, legend_loc='best', log_y=False, y_tick_format=None)[source]¶ Bases:
object
A top level plot contains a list of subplots, each of which contain a list of data objects to draw
-
__init__
(data_list, secondary_y=None, title=None, xlabel=None, ylabel=None, zlabel=None, date_lines=None, horizontal_lines=None, vertical_lines=None, xlim=None, ylim=None, height_ratio=1.0, display_legend=True, legend_loc='best', log_y=False, y_tick_format=None)[source]¶ - Parameters
data_list – A list of objects to draw. Each element can contain XYData, XYZData, TimeSeries, TradeBarSeries, BucketedValues or TradeSet
secondary_y (list of str, optional) – A list of objects to draw on the secondary y axis
title (str, optional) – Title to show for this subplot. Default None
zlabel (str, optional) – Only applicable to 3d subplots. Default None
date_lines (list of
DateLine
, optional) – A list of DateLine objects to draw as vertical lines. Only applicable when x axis is datetime. Default Nonehorizontal_lines (list of
HorizontalLine
, optional) – A list of HorizontalLine objects to draw on the plot. Default Nonevertical_lines (list of
VerticalLine
, optional) – A list of VerticalLine objects to draw on the plotxlim (tuple of datetime or float, optional) – x limits for the plot as a tuple of numpy datetime objects when x-axis is datetime, or tuple of floats. Default None
ylim (tuple of float, optional) – y limits for the plot. Tuple of floats. Default None
height_ratio (float, optional) – If you have more than one subplot on a plot, use height ratio to determine how high each subplot should be. For example, if you set height_ratio = 0.75 for the first subplot and 0.25 for the second, the first will be 3 times taller than the second one. Default 1.0
display_legend (bool, optional) – Whether to show a legend on the plot. Default True
legend_loc (str, optional) – Location for the legend. Default ‘best’
log_y (bool, optional) – whether the y axis should be logarithmic. Default False
y_tick_format (str, optional) – Format string to use for y axis labels. For example, you can decide to use fixed notation instead of scientific notation or change number of decimal places shown. Default None
-
-
class
pyqstrat.plot.
TimeSeries
(name, timestamps, values, plot_type='line', line_type='solid', line_width=None, color=None, marker=None, marker_size=50, marker_color='red')[source]¶ Bases:
object
Data in a subplot where x is an array of numpy datetimes and y is a numpy array of floats
-
__init__
(name, timestamps, values, plot_type='line', line_type='solid', line_width=None, color=None, marker=None, marker_size=50, marker_color='red')[source]¶ Args: name: Name to show in plot legend timestamps: pandas Series or numpy array of datetime64 values: pandas Series or numpy array of floats plot_type: ‘line’ or ‘scatter’ marker: If set, show a marker at each value in values. See matplotlib marker types
-
-
class
pyqstrat.plot.
TradeBarSeries
(name, timestamps, o, h, l, c, v=None, vwap=None, colorup='darkgreen', colordown='#F2583E')[source]¶ Bases:
object
Data in a subplot that contains open, high, low, close, volume bars. volume is optional.
-
class
pyqstrat.plot.
TradeSet
(name, trades, marker='P', marker_color=None, marker_size=50)[source]¶ Bases:
object
Data for subplot that contains a set of trades along with marker properties for these trades
-
class
pyqstrat.plot.
VerticalLine
(x, name=None, line_type='dashed', color=None)[source]¶ Bases:
object
Draws a vertical line on a subplot where x axis is not a date-time axis
-
class
pyqstrat.plot.
XYData
(name, x, y, plot_type='line', line_type='solid', line_width=None, color=None, marker=None, marker_size=50, marker_color='red')[source]¶ Bases:
object
Data in a subplot that has x and y values that are both arrays of floats
-
class
pyqstrat.plot.
XYZData
(name, x, y, z, plot_type='surface', marker='X', marker_size=50, marker_color='red', interpolation='linear', cmap='viridis')[source]¶ Bases:
object
Data in a subplot that has x, y and z values that are all floats
-
__init__
(name, x, y, z, plot_type='surface', marker='X', marker_size=50, marker_color='red', interpolation='linear', cmap='viridis')[source]¶ - Parameters
x – pandas series or numpy array of floats
y – pandas series or numpy array of floats
z – pandas series or numpy array of floats
plot_type – surface or contour (default surface)
marker – Adds a marker to each point in x, y, z to show the actual data used for interpolation. You can set this to None to turn markers off.
interpolation – Can be ‘linear’, ‘nearest’ or ‘cubic’ for plotting z points between the ones passed in. See scipy.interpolate.griddata for details
cmap – Colormap to use (default viridis). See matplotlib colormap for details
-
-
pyqstrat.plot.
draw_3d_plot
(ax, x, y, z, plot_type, marker='X', marker_size=50, marker_color='red', interpolation='linear', cmap='viridis')[source]¶ Draw a 3d plot. See XYZData class for explanation of arguments
>>> points = np.random.rand(1000, 2) >>> x = np.random.rand(10) >>> y = np.random.rand(10) >>> z = x ** 2 + y ** 2 >>> if has_display(): ... fig, ax = plt.subplots() ... draw_3d_plot(ax, x = x, y = y, z = z, plot_type = 'contour', interpolation = 'linear')
-
pyqstrat.plot.
draw_boxplot
(ax, names, values, proportional_widths=True, notched=False, show_outliers=True, show_means=True, show_all=True)[source]¶ Draw a boxplot. See BucketedValues class for explanation of arguments
-
pyqstrat.plot.
draw_candlestick
(ax, index, o, h, l, c, v, vwap, colorup='darkgreen', colordown='#F2583E')[source]¶ Draw candlesticks given parrallel numpy arrays of o, h, l, c, v values. v is optional. See TradeBarSeries class __init__ for argument descriptions.
-
pyqstrat.plot.
draw_date_line
(ax, plot_timestamps, date, linestyle, color)[source]¶ Draw vertical line on a subplot with datetime x axis
-
pyqstrat.plot.
draw_horizontal_line
(ax, y, linestyle, color)[source]¶ Draw horizontal line on a subplot
-
pyqstrat.plot.
draw_poly
(ax, left, bottom, top, right, facecolor, edgecolor, zorder)[source]¶ Draw a set of polygrams given parrallel numpy arrays of left, bottom, top, right points
-
pyqstrat.plot.
get_date_formatter
(plot_timestamps, date_format)[source]¶ Create an appropriate DateFormatter for x axis labels. If date_format is set to None, figures out an appropriate date format based on the range of timestamps passed in
-
pyqstrat.plot.
trade_sets_by_reason_code
(trades, marker_props={'backtest end': {'color': 'green', 'size': 50, 'symbol': '*'}, 'enter long': {'color': 'blue', 'size': 50, 'symbol': 'P'}, 'enter short': {'color': 'red', 'size': 50, 'symbol': 'P'}, 'exit long': {'color': 'blue', 'size': 50, 'symbol': 'X'}, 'exit short': {'color': 'red', 'size': 50, 'symbol': 'X'}, 'none': {'color': 'green', 'size': 50, 'symbol': 'o'}, 'roll future': {'color': 'green', 'size': 50, 'symbol': '>'}}, remove_missing_properties=True)[source]¶ Returns a list of TradeSet objects. Each TradeSet contains trades with a different reason code. The markers for each TradeSet are set by looking up marker properties for each reason code using the marker_props argument:
- Parameters
trades (list of
Trade
) – We look up reason codes using the reason code on the corresponding orders(dict of str (marker_props) – dict, optional): Dictionary from reason code string -> dictionary of marker properties. See ReasonCode.MARKER_PROPERTIES for example. Default ReasonCode.MARKER_PROPERTIES
remove_missing_properties (bool, optional) – If set, we remove any reason codes that dont’ have marker properties set. Default True
pyqstrat.evaluator module¶
-
class
pyqstrat.evaluator.
Evaluator
(initial_metrics)[source]¶ Bases:
object
You add functions to the evaluator that are dependent on the outputs of other functions. The evaluator will call these functions in the right order so dependencies are computed first before the functions that need their output. You can retrieve the output of a metric using the metric member function
>>> evaluator = Evaluator(initial_metrics={'x' : np.array([1, 2, 3]), 'y' : np.array([3, 4, 5])}) >>> evaluator.add_metric('z', lambda x, y: sum(x, y), dependencies=['x', 'y']) >>> evaluator.compute() >>> evaluator.metric('z') array([ 9, 10, 11])
-
__init__
(initial_metrics)[source]¶ Inits Evaluator with a dictionary of initial metrics that are used to compute subsequent metrics
- Parameters
initial_metrics – a dictionary of string name -> metric. metric can be any object including a scalar, an array or a tuple
-
compute
(metric_names=None)[source]¶ Compute metrics using the internal dependency graph
- Parameters
metric_names – an array of metric names. If not passed in, evaluator will compute and store all metrics
-
-
pyqstrat.evaluator.
compute_amean
(returns, periods_per_year)[source]¶ Computes arithmetic mean of a return array, ignoring NaNs
- Parameters
returns – a numpy array of floats representing returns at any frequency
- Returns
a float
>>> compute_amean(np.array([0.003, 0.004, np.nan]), 252) 0.882
-
pyqstrat.evaluator.
compute_annual_returns
(timestamps, returns, periods_per_year)[source]¶ Takes the output of compute_bucketed_returns and returns geometric mean of returns by year
- Returns
A tuple with the first element being an array of years (integer) and the second element an array of annualized returns for those years
-
pyqstrat.evaluator.
compute_bucketed_returns
(timestamps, returns)[source]¶ Bucket returns by year
- Returns
- A tuple with the first element being a list of years and the second a list of
numpy arrays containing returns for each corresponding year
-
pyqstrat.evaluator.
compute_calmar
(returns_3yr, periods_per_year, mdd_pct_3yr)[source]¶ Compute Calmar ratio, which is the annualized return divided by max drawdown over the last 3 years
-
pyqstrat.evaluator.
compute_dates_3yr
(timestamps)[source]¶ Given an array of numpy datetimes, return those that are within 3 years of the last date in the array
-
pyqstrat.evaluator.
compute_equity
(timestamps, starting_equity, returns)[source]¶ Given starting equity, timestamps and returns, create a numpy array of equity at each date
-
pyqstrat.evaluator.
compute_gmean
(timestamps, returns, periods_per_year)[source]¶ Computes geometric mean of an array of returns
- Parameters
returns – a numpy array of returns, can contain nans
periods_per_year – Used for annualizing returns
- Returns
a float
>>> round(compute_gmean(np.array(['2015-01-01', '2015-03-01', '2015-05-01'], dtype = 'M8[D]'), np.array([0.001, 0.002, 0.003]), 252.), 6) 0.018362
-
pyqstrat.evaluator.
compute_mar
(returns, periods_per_year, mdd_pct)[source]¶ Compute MAR ratio, which is annualized return divided by biggest drawdown since inception.
-
pyqstrat.evaluator.
compute_maxdd_date
(rolling_dd_dates, rolling_dd)[source]¶ Compute date of max drawdown given numpy array of timestamps, and corresponding rolling dd percentages
-
pyqstrat.evaluator.
compute_maxdd_date_3yr
(rolling_dd_3yr_timestamps, rolling_dd_3yr)[source]¶ Compute max drawdown date over the last 3 years
-
pyqstrat.evaluator.
compute_maxdd_pct
(rolling_dd)[source]¶ Compute max drawdown percentage given a numpy array of rolling drawdowns, ignoring NaNs
-
pyqstrat.evaluator.
compute_maxdd_pct_3yr
(rolling_dd_3yr)[source]¶ Compute max drawdown percentage over the last 3 years
-
pyqstrat.evaluator.
compute_maxdd_start
(rolling_dd_dates, rolling_dd, mdd_date)[source]¶ Compute date when max drawdown starts, given numpy array of timestamps corresponding rolling dd percentages and date that max dd starts
-
pyqstrat.evaluator.
compute_maxdd_start_3yr
(rolling_dd_3yr_timestamps, rolling_dd_3yr, mdd_date_3yr)[source]¶ Comput max drawdown start date over the last 3 years
-
pyqstrat.evaluator.
compute_num_periods
(timestamps, periods_per_year)[source]¶ - Given an array of timestamps, we compute how many periods there are between the first and last element, where the length
of a period is defined by periods_per_year. For example, if there are 6 periods per year, then each period would be approx. 2 months long.
- Parameters
timestamps (np.ndarray of np.datetime64) – a numpy array of returns, can contain nans
periods_per_year (int) – number of periods between first and last return
- Returns
a float
>>> compute_num_periods(np.array(['2015-01-01', '2015-03-01', '2015-05-01'], dtype = 'M8[D]'), 6) 2.0
-
pyqstrat.evaluator.
compute_periods_per_year
(timestamps)[source]¶ - Computes trading periods per year for an array of numpy datetime64’s.
E.g. if most of the timestamps are separated by 1 day, will return 252.
- Parameters
timestamps – a numpy array of datetime64’s
- Returns
a float
>>> compute_periods_per_year(np.array(['2018-01-01', '2018-01-02', '2018-01-03', '2018-01-09'], dtype = 'M8[D]')) 252.0 >>> round(compute_periods_per_year(np.array(['2018-01-01 10:00', '2018-01-01 10:05', '2018-01-01 10:10'], dtype = 'M8[m]')), 2) 72576.05
-
pyqstrat.evaluator.
compute_return_metrics
(timestamps, rets, starting_equity, leading_non_finite_to_zeros=False, subsequent_non_finite_to_zeros=True)[source]¶ Compute a set of common metrics using returns (for example, of an instrument or a portfolio)
- Parameters
timestamps (np.array of datetime64) – Timestamps for the returns
rets (nd.array of float) – The returns, use 0.01 for 1%
starting_equity (float) – Starting equity value in your portfolio
leading_non_finite_to_zeros (bool, optional) – If set, we replace leading nan, inf, -inf returns with zeros. For example, you may need a warmup period for moving averages. Default False
subsequent_non_finite_to_zeros (bool, optional) – If set, we replace any nans that follow the first non nan value with zeros. There may be periods where you have no prices but removing these returns would result in incorrect annualization. Default True
- Returns
An Evaluator object containing computed metrics off the returns passed in. If needed, you can add your own metrics to this object based on the values of existing metrics and recompute the Evaluator. Otherwise, you can just use the output of the evaluator using the metrics function.
>>> timestamps = np.array(['2015-01-01', '2015-03-01', '2015-05-01', '2015-09-01'], dtype = 'M8[D]') >>> rets = np.array([0.01, 0.02, np.nan, -0.015]) >>> starting_equity = 1.e6 >>> ev = compute_return_metrics(timestamps, rets, starting_equity) >>> metrics = ev.metrics() >>> assert(round(metrics['gmean'], 6) == 0.021061) >>> assert(round(metrics['sharpe'], 6) == 0.599382) >>> assert(all(metrics['returns_3yr'] == np.array([0.01, 0.02, 0, -0.015])))
-
pyqstrat.evaluator.
compute_returns_3yr
(timestamps, returns)[source]¶ Given an array of numpy datetimes and an array of returns, return those that are within 3 years of the last date in the datetime array
-
pyqstrat.evaluator.
compute_rolling_dd
(timestamps, equity)[source]¶ Compute numpy array of rolling drawdown percentage
- Parameters
timestamps – numpy array of datetime64
equity – numpy array of equity
-
pyqstrat.evaluator.
compute_rolling_dd_3yr
(timestamps, equity)[source]¶ Compute rolling drawdowns over the last 3 years
-
pyqstrat.evaluator.
compute_sharpe
(returns, amean, periods_per_year)[source]¶ Note that this does not take into risk free returns so it’s really a sharpe0, i.e. assumes risk free returns are 0
- Parameters
returns – a numpy array of returns
amean – arithmetic mean of returns
periods_per_year – number of trading periods per year
>>> round(compute_sharpe(np.array([0.001, -0.001, 0.002]), 0.001, 252), 6) 0.050508
-
pyqstrat.evaluator.
compute_sortino
(returns, amean, periods_per_year)[source]¶ Note that this assumes target return is 0.
- Parameters
returns – a numpy array of returns
amean – arithmetic mean of returns
periods_per_year – number of trading periods per year
>>> print(round(compute_sortino(np.array([0.001, -0.001, 0.002]), 0.001, 252), 6)) 0.133631
-
pyqstrat.evaluator.
compute_std
(returns)[source]¶ Computes standard deviation of an array of returns, ignoring nans
-
pyqstrat.evaluator.
display_return_metrics
(metrics, float_precision=3)[source]¶ Creates a dataframe making it convenient to view the output of the metrics obtained using the compute_return_metrics function.
- Parameters
float_precision – Change if you want to display floats with more or less significant figures than the default, 3 significant figures.
- Returns
A one row dataframe with formatted metrics.
-
pyqstrat.evaluator.
handle_non_finite_returns
(timestamps, rets, leading_non_finite_to_zeros, subsequent_non_finite_to_zeros)[source]¶ >>> np.set_printoptions(formatter={'float': '{: .6g}'.format}) >>> timestamps = np.arange(np.datetime64('2019-01-01'), np.datetime64('2019-01-07')) >>> rets = np.array([np.nan, np.nan, 3, 4, np.nan, 5]) >>> handle_non_finite_returns(timestamps, rets, leading_non_finite_to_zeros = False, subsequent_non_finite_to_zeros = True) (array(['2019-01-03', '2019-01-04', '2019-01-05', '2019-01-06'], dtype='datetime64[D]'), array([ 3, 4, 0, 5])) >>> handle_non_finite_returns(timestamps, rets, leading_non_finite_to_zeros = True, subsequent_non_finite_to_zeros = False) (array(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04', '2019-01-06'], dtype='datetime64[D]'), array([ 0, 0, 3, 4, 5])) >>> handle_non_finite_returns(timestamps, rets, leading_non_finite_to_zeros = False, subsequent_non_finite_to_zeros = False) (array(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04', '2019-01-06'], dtype='datetime64[D]'), array([ 0, 0, 3, 4, 5])) >>> rets = np.array([1, 2, 3, 4, 4.5, 5]) >>> handle_non_finite_returns(timestamps, rets, leading_non_finite_to_zeros = False, subsequent_non_finite_to_zeros = True) (array(['2019-01-01', '2019-01-02', '2019-01-03', '2019-01-04', '2019-01-05', '2019-01-06'], dtype='datetime64[D]'), array([ 1, 2, 3, 4, 4.5, 5]))
pyqstrat.pyqstrat_cpp module¶
-
class
pyqstrat.pyqstrat_cpp.
Aggregator
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
AllOpenInterestAggregator
¶ Bases:
pyqstrat.pyqstrat_cpp.Aggregator
Writes out all open interest records
-
__call__
()¶ Add an open interest record to be written to disk at some point
- Parameters
oi (
OpenInterestRecord
) –line_number (int) – The line number of the source file that this trade came from. Used for debugging
-
__init__
()¶ - Parameters
writer_creator – A function that takes an output_file_prefix, schema, create_batch_id and max_batch_size and returns an object implementing the
Writer
interfaceoutput_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
batch_size (int, optional) – If set, we will write a batch to disk every time we have this many records queued up. Defaults to 2.1 billion
timestamp_unit (Schema.Type, optional) – Whether timestamps are measured as milliseconds or microseconds since the unix epoch. Defaults to Schema.TIMESTAMP_MILLI
-
-
class
pyqstrat.pyqstrat_cpp.
AllOtherAggregator
¶ Bases:
pyqstrat.pyqstrat_cpp.Aggregator
Writes out any records that are not trades, quotes or open interest
-
__call__
()¶ Add a record to be written to disk at some point
- Parameters
other (
OtherRecord
) –line_number (int) – The line number of the source file that this trade came from. Used for debugging
-
__init__
()¶ - Parameters
writer_creator – A function that takes an output_file_prefix, schema, create_batch_id and max_batch_size and returns an object implementing the
Writer
interfaceoutput_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
batch_size (int, optional) – If set, we will write a batch to disk every time we have this many records queued up. Defaults to 2.1 billion
timestamp_unit (Schema.Type, optional) – Whether timestamps are measured as milliseconds or microseconds since the unix epoch. Defaults to Schema.TIMESTAMP_MILLI
-
-
class
pyqstrat.pyqstrat_cpp.
AllQuoteAggregator
¶ Bases:
pyqstrat.pyqstrat_cpp.Aggregator
Writes out every quote we see
-
__call__
()¶ Add a quote record to be written to disk at some point
- Parameters
quote (
QuoteRecord
) –line_number (int) – The line number of the source file that this trade came from. Used for debugging
-
__init__
()¶ - Parameters
writer_creator – A function that takes an output_file_prefix, schema, create_batch_id and max_batch_size and returns an object implementing the
Writer
interfaceoutput_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
batch_size – If set, we will write a batch to disk every time we have this many records queued up. Defaults to 2.1 billion
-
-
class
pyqstrat.pyqstrat_cpp.
AllQuotePairAggregator
¶ Bases:
pyqstrat.pyqstrat_cpp.Aggregator
Writes out every quote pair we find
-
__call__
()¶ Add a quote pair record to be written to disk at some point
- Parameters
quote_pair (
QuoteRecord
) – line_number (int): The line number of the source file that this trade came from. Used for debugging
-
__init__
()¶ - Parameters
writer_creator – A function that takes an output_file_prefix, schema, create_batch_id and max_batch_size and returns an object implementing the
Writer
interfaceoutput_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
batch_size (int, optional) – If set, we will write a batch to disk every time we have this many records queued up. Defaults to 2.1 billion
timestamp_unit (Schema.Type, optional) – Whether timestamps are measured as milliseconds or microseconds since the unix epoch.
to Schema.TIMESTAMP_MILLI (Defaults) –
-
-
class
pyqstrat.pyqstrat_cpp.
AllTradeAggregator
¶ Bases:
pyqstrat.pyqstrat_cpp.Aggregator
Writes out every trade we see
-
__call__
()¶ Add a trade record to be written to disk at some point
- Parameters
trade (
TradeRecord
) –line_number (int) – The line number of the source file that this trade came from. Used for debugging
-
__init__
()¶ - Parameters
writer_creator – A function that takes an output_file_prefix, schema, create_batch_id and max_batch_size and returns an object implementing the
Writer
interfaceoutput_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
batch_size (int, optional) – If set, we will write a batch to disk every time we have this many records queued up. Defaults to 2.1 billion
timestamp_unit (Schema.Type, optional) – Whether timestamps are measured as milliseconds or microseconds since the unix epoch. Defaults to Schema.TIMESTAMP_MILLI
-
-
class
pyqstrat.pyqstrat_cpp.
ArrowWriter
¶ Bases:
pyqstrat.pyqstrat_cpp.Writer
A subclass of
Writer
that batches of records to a disk file in the Apache arrow format. See Apache arrow for details-
__init__
()¶ - Parameters
output_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
schema (
Schema
) – A schema object containing the names and datatypes of each field we want to save in a recordcreate_batch_id_file (bool, optional) – Whether to create a corresponding file that contains a map from batch id -> batch number so we can easily lookup a batch number and then retrieve it from disk. Defaults to False
max_batch_size (int, optional) – If set, when we get this many records, we write out a batch of records to disk. May be necessary when we are creating large output files, to avoid running out of memory when reading and writing. Defaults to -1
-
add_record
()¶ Add a record that will be written to disk at some point.
- Parameters
line_number (int) – The line number of the source file that this trade came from. Used for debugging
tuple (tuple) – Must correspond to the schema defined in the constructor. For example, if the schema has a bool and a float, the tuple could be (False, 0.5)
-
close
()¶ Close the writer and flush any remaining data to disk
- Parameters
success (bool, optional) – If set to False, we had some kind of exception and are cleaning up. Tells the function to not indicate the file was written successfully, for example by renaming a temp file to the actual filename. Defaults to True
-
write_batch
()¶ Write a batch of records to disk. The batch can have an optional string id so we can later retrieve just this batch of records without reading the whole file.
- Parameters
batch_id (str, optional) – An identifier which can later be used to retrieve this batch from disk. Defaults to “”
-
-
class
pyqstrat.pyqstrat_cpp.
ArrowWriterCreator
¶ Bases:
pyqstrat.pyqstrat_cpp.WriterCreator
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
BadLineHandler
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
CheckFields
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
FileProcessor
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
FixedWidthTimeParser
¶ Bases:
pyqstrat.pyqstrat_cpp.TimestampParser
A helper class that takes a string formatted as HH:MM:SS.xxx and parses it into number of milliseconds or micros since the beginning of the day
-
__call__
()¶ - Parameters
time (str) – A string like “2018-01-01 08:35:22.132”
- Returns
Milliseconds or microseconds since Unix epoch
- Return type
int
-
__init__
()¶ - Parameters
micros (bool, optional) – Whether to return timestamp in millisecs or microsecs since 1970. Default false
hours_start (int, optional) – index where the hour starts in the timestamp string. Default -1
hours_size (int, optional) – number of characters used for the hour
minutes_start (int, optional) –
minutes_size (int, optional) –
seconds_start (int, optional) –
seconds_size (int, optional) –
millis_start (int, optional) –
millis_size (int, optional) –
micros_start (int, optional) –
micros_size (int, optional) –
-
-
class
pyqstrat.pyqstrat_cpp.
FormatTimestampParser
¶ Bases:
pyqstrat.pyqstrat_cpp.TimestampParser
- Helper class that parses timestamps according to the strftime format string passed in. strftime is slow so
use
FixedWithTimeParser
if your timestamp has a fixed format such as “HH:MM:SS….”
-
__call__
()¶ - Parameters
time (str) – The timestamp to parse
- Returns
Number of millis or micros since epoch
- Return type
int
-
__init__
()¶ - Parameters
base_date (int) – Sometimes the timestamps in a file contain time only and the name of a file contains the date. In these cases, pass in the date as number of millis or micros from the epoch to the start of that date. If the timestamp has date also, pass in 0 here.
time_format (str, optional) – strftime format string for parsing the timestamp. Defaults to “%H:%M:%S”
micros (bool, optional) – If this is set, we will parse and store microseconds. Otherwise we will parse and store milliseconds. Defaults to True
-
class
pyqstrat.pyqstrat_cpp.
IsFieldInList
¶ Bases:
pyqstrat.pyqstrat_cpp.CheckFields
Simple utility class to check whether the value of fields[flag_idx] is in any of flag_values
-
__call__
()¶ - Parameters
flag_values – a vector of strings containing possible values for the field
- Returns
a boolean
-
__init__
()¶ - Parameters
fields – a vector of strings
flag_idx – the index of fields to check
-
-
class
pyqstrat.pyqstrat_cpp.
LineFilter
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
MissingDataHandler
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
OpenInterestRecord
¶ Bases:
pyqstrat.pyqstrat_cpp.Record
Open interest for a future or option. Usually one record per instrument at the beginning of the day
-
id
¶ Represents a symbol or instrument id, for example, for an option you may concantenate underlying symbol, expiration, strike, put or call to uniquely identify the instrument
- Type
str
-
timestamp
¶ Trade time, in milliseconds or microseconds since 1/1/1970
- Type
int
-
qty
¶ Trade quantity
- Type
float
-
metadata
¶ A string representing any extra information you want to save, such as exchange, or special trade conditions
- Type
str
-
__init__
()¶
-
id
-
metadata
-
qty
-
timestamp
-
-
class
pyqstrat.pyqstrat_cpp.
OtherRecord
¶ Bases:
pyqstrat.pyqstrat_cpp.Record
Any other data you want to store from market data besides trades, quotes and open interest. You can capture any important fields in the metadata attribute
-
id
¶ Represents a symbol or instrument id, for example, for an option you may concantenate underlying symbol, expiration, strike, put or call to uniquely identify the instrument
- Type
str
-
timestamp
¶ trade time, in milliseconds or microseconds since 1/1/1970
- Type
int
-
metadata
¶ a string representing any extra information you want to save, such as exchange, or special trade conditions
- Type
str
-
__init__
()¶
-
id
-
metadata
-
timestamp
-
-
class
pyqstrat.pyqstrat_cpp.
PriceQtyMissingDataHandler
¶ Bases:
pyqstrat.pyqstrat_cpp.MissingDataHandler
A helper class that takes a Record as an input, checks whether its a trade or a quote or any open interest record, and if any of the prices or quantities are 0, sets them to NAN
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
PrintBadLineHandler
¶ Bases:
pyqstrat.pyqstrat_cpp.BadLineHandler
A helper class that takes in lines we cannot parse and either prints them and continues or raises an Exception
-
__call__
()¶ - Parameters
line_number (int) – Line number of the input file that corresponds to this line (for debugging)
line (str) – The actual line that failed to parse
exception (Exception) – The exception that caused us to fail to parse this line.
-
__init__
()¶ - Parameters
raise (bool, optional) – Whether to raise an exception every time this is called or just print debugging info. Defaults to False
-
-
class
pyqstrat.pyqstrat_cpp.
QuoteRecord
¶ Bases:
pyqstrat.pyqstrat_cpp.Record
A parsed quote record that we can save to disk
-
id
¶ Represents a symbol or instrument id, for example, for an option you may concantenate underlying symbol, expiration, strike, put or call to uniquely identify the instrument
- Type
str
-
timestamp
¶ Trade time, in milliseconds or microseconds since 1/1/1970
- Type
int
-
bid
¶ If True, this is a bid quote, otherwise it is an offer
- Type
bool
-
qty
¶ Trade quantity
- Type
float
-
price
¶ Trade price
- Type
float
-
metadata
¶ A string representing any extra information you want to save, such as exchange, or special trade conditions
- Type
str
-
__init__
()¶
-
bid
-
id
-
metadata
-
price
-
qty
-
timestamp
-
-
class
pyqstrat.pyqstrat_cpp.
QuoteTOBAggregator
¶ Bases:
pyqstrat.pyqstrat_cpp.Aggregator
Aggregate top of book quotes to top of book records. If you specify a frequency such as “5m”, we will calculate a record every 5 minutes which has the top of book at the end of that bar. If no frequency is specified, we will create a top of book every time a quote comes in. We assume that the quotes are all top of book quotes and are written in pairs so we have a bid quote followed by a offer quote with the same timestamp or vice versa
-
__call__
()¶ Add a quote record to be written to disk at some point
- Parameters
quote (
QuoteRecord
) –line_number (int) – The line number of the source file that this trade came from. Used for debugging
-
__init__
()¶ - Parameters
writer_creator – A function that takes an output_file_prefix, schema, create_batch_id and max_batch_size and returns an object implementing the
Writer
interfaceoutput_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
frequency (str, optional) – A string like “5m” indicating the bar size is 5 mins. Units can be s,m,h or d for second, minute, hour or day. Defaults to “5m”. If you set this to “”, each tick will be recorded.
batch_by_id (bool, optional) – If set, we will create one batch for each id. This will allow us to retrieve all records for a single
by reading a single batch. Defaults to True. (instrument) –
batch_size (int, optional) – If set, we will write a batch to disk every time we have this many records queued up. Defaults to 2.1 billion
timestamp_unit (Schema.Type, optional) – Whether timestamps are measured as milliseconds or microseconds since the unix epoch. Defaults to Schema.TIMESTAMP_MILLI
-
close
()¶ Flush all unwritten records to the Writer, which writes them to disk when its close function is called
-
-
class
pyqstrat.pyqstrat_cpp.
Record
¶ Bases:
pybind11_builtins.pybind11_object
-
__init__
¶ Initialize self. See help(type(self)) for accurate signature.
-
-
class
pyqstrat.pyqstrat_cpp.
RecordFieldParser
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
RecordFilter
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
RecordGenerator
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
RecordParser
¶ Bases:
pybind11_builtins.pybind11_object
-
__init__
()¶
-
add_line
()¶ - Parameters
line (str) – The line we need to parse
-
-
class
pyqstrat.pyqstrat_cpp.
RegExLineFilter
¶ Bases:
pyqstrat.pyqstrat_cpp.LineFilter
A helper class that filters lines from the input file based on a regular expression. Note that regular expressions are slow, so if you just need to match specific strings, use a string matching filter instead.
-
__call__
()¶ - Parameters
line (str) – The string that the regular expression should match.
- Returns
Whether the regex matched
- Return type
bool
-
__init__
()¶ - Parameters
pattern (str) – The regex pattern to match. This follows C++ std::regex pattern matching rules as opposed to python
-
-
class
pyqstrat.pyqstrat_cpp.
Schema
¶ Bases:
pybind11_builtins.pybind11_object
Describes a list of field names and data types for writing records to disk
-
types
¶ A list of (str, type) tuples describing a record with the name of each field and its datatype
-
BOOL
= Type.BOOL¶
-
FLOAT32
= Type.FLOAT32¶
-
FLOAT64
= Type.FLOAT64¶
-
INT32
= Type.INT32¶
-
INT64
= Type.INT64¶
-
STRING
= Type.STRING¶
-
TIMESTAMP_MICRO
= Type.TIMESTAMP_MICRO¶
-
TIMESTAMP_MILLI
= Type.TIMESTAMP_MILLI¶
-
class
Type
¶ Bases:
pybind11_builtins.pybind11_object
-
BOOL
= Type.BOOL¶
-
FLOAT32
= Type.FLOAT32¶
-
FLOAT64
= Type.FLOAT64¶
-
INT32
= Type.INT32¶
-
INT64
= Type.INT64¶
-
STRING
= Type.STRING¶
-
TIMESTAMP_MICRO
= Type.TIMESTAMP_MICRO¶
-
TIMESTAMP_MILLI
= Type.TIMESTAMP_MILLI¶
-
__init__
()¶
-
-
__init__
()¶
-
types
-
-
class
pyqstrat.pyqstrat_cpp.
SubStringLineFilter
¶ Bases:
pyqstrat.pyqstrat_cpp.LineFilter
A helper class that will check if a line matches any of a set of strings
-
__call__
()¶ - Parameters
line (str) – We check if any of the patterns are present in this string
- Returns
Whether any of the patterns were present
- Return type
bool
-
__init__
()¶ - Parameters
patterns (list of str) – The list of strings to match against
-
-
class
pyqstrat.pyqstrat_cpp.
TextFileDecompressor
¶ Bases:
pyqstrat.pyqstrat_cpp.RecordGenerator
A helper function that takes a filename and its compression type, and returns a function that we can use to iterate over lines in that file
-
__call__
()¶ - Parameters
filename (str) – The file to read
compression (str) – One of “” for uncompressed files, “gzip”, “bz2” or “lzip”
- Returns
A function that takes an empty string as input, and fills in that string. The function should return False EOF has been reached, True otherwise
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
TextFileProcessor
¶ Bases:
pyqstrat.pyqstrat_cpp.FileProcessor
A helper class that takes text based market data files and creates parsed and aggregated quote, trade, open interest, and other files from them.
-
__call__
()¶ - Parameters
input_filename (str) – The file to read
compression (str) – One of “” for uncompressed files, “gzip”, “bz2” or “lzip”
- Returns
Number of lines processed
- Return type
int
-
__init__
()¶ - Parameters
record_generator – A function that takes a filename and its compression type, and returns a function that we can use to iterate over lines in that file
line_filter – A function that takes a line (str) as input and returns whether we should parse it or discard it
record_parser – A function that takes a line (str) as input and returns a
Record
objectbad_line_handler – A function that takes a line that failed to parse and returns a
Record
object or Nonerecord_filter – A function that takes a parsed Record object and returns whether we should keep it or discard it
missing_data_handler – A function that takes a parsed Record object and deals with missing data, for example, by converting 0’s to NANs
aggregators – A vector of functions that each take a parsed Record object and aggregate it.
skip_rows (int, optional) – Number of rows to skip in the file before starting to read it. Defaults to 1 to ignore a header line
-
-
class
pyqstrat.pyqstrat_cpp.
TextOpenInterestParser
¶ Bases:
pyqstrat.pyqstrat_cpp.RecordFieldParser
Helper class that parses an open interest record from a list of fields (strings)
-
__call__
()¶ - Parameters
fields (list of str) – A list of fields representing the record
- Returns
or None if this record is not an open interest record
- Return type
-
__init__
()¶ - Parameters
is_open_interest – A function that takes a list of strings as input and returns a bool if the fields represent an open interest record
base_date (int) – If the timestamp in the files does not have a date component, pass in the date as number of millis or micros since the epoch
timestamp_indices (list of int) – Index of the timestamp fields within the record. For example, date and time could be in different fields. We add the result of each timestamp field to get the final timestamp
qty_idx (int) – Index of the quote size field
id_field_indices (list of str) – Indices of the fields identifying an instrument. For example, for a future this could be symbol and expiry. These fields will be concatenated with a separator and placed in the id field in the record
meta_field_indices (list of str) – Indices of additional fields you want to store. For example, the exchange.
timestamp_parsers – A list of function that takes a timestamp as a string and returns number of millis or micros since the epoch
strip_id (bool, optional) – If we want to strip any whitespace from the id fields before concatenating them. Defaults to True
strip_meta (bool, optional) – If we want to strip any whitespace from the meta fields before concatenating them. Defaults to True
-
-
class
pyqstrat.pyqstrat_cpp.
TextOtherParser
¶ Bases:
pyqstrat.pyqstrat_cpp.RecordFieldParser
Helper class that parses a record that contains information other than a quote, trade or open interest record
-
__call__
()¶ - Parameters
fields (list of str) – a list of fields representing the record
- Returns
- Return type
-
__init__
()¶ - Parameters
is_other – A function that takes a list of strings as input and returns a bool if we want to parse this record
base_date (int) – If the timestamp in the files does not have a date component, pass in the date as number of millis or micros since the epoch
timestamp_indices (list of int) – Index of the timestamp fields within the record. For example, date and time could be in different fields. We add the result of each timestamp field to get the final timestamp
id_field_indices (list of str) – Indices of the fields identifying an instrument. For example, for a future this could be symbol and expiry. These fields will be concatenated with a separator and placed in the id field in the record
meta_field_indices (list of str) – Indices of additional fields you want to store. For example, the exchange.
timestamp_parsers – A list of functions that take a timestamp as a string and returns number of millis or micros since the epoch
strip_id (bool, optional) – If we want to strip any whitespace from the id fields before concatenating them. Defaults to True
strip_meta (bool, optional) – If we want to strip any whitespace from the meta fields before concatenating them. Defaults to True
-
-
class
pyqstrat.pyqstrat_cpp.
TextQuotePairParser
¶ Bases:
pyqstrat.pyqstrat_cpp.RecordFieldParser
Helper class that parses a quote containing bid / ask in the same record from a list of fields (strings)
-
__call__
()¶ - Parameters
fields (list of str) – A list of fields representing the record
Returns –
QuotePairRecord – Or None if this field is not a quote pair
-
__init__
()¶ - Parameters
is_quote_pair – a function that takes a list of strings as input and returns a bool if the fields represent a quote pair
base_date (int) – if the timestamp in the files does not have a date component, pass in the date as number of millis or micros since the epoch
timestamp_indices (list of int) – Index of the timestamp fields within the record. For example, date and time could be in different fields. We add the result of each timestamp field to get the final timestamp
bid_price_idx (int) – index of the field that contains the bid price
bid_qty_idx (int) – index of the field that contains the bid quantity
ask_price_idx (int) – index of the field that contains the ask price
ask_qty_idx (int) – index of the field that contains the ask quantity
id_field_indices (list of str) – indices of the fields identifying an instrument. For example, for a future this could be symbol and expiry. These fields will be concatenated with a separator and placed in the id field in the record.
meta_field_indices (list of str) – indices of additional fields you want to store. For example, the exchange.
timestamp_parsers – a list of functions that takes a timestamp as a string and returns number of millis or micros since the epoch
price_multiplier – (float, optional): sometimes the price in a file could be in hundredths of cents, and we divide by this to get dollars. Defaults to 1.0
strip_id (bool, optional) – if we want to strip any whitespace from the id fields before concatenating them. Defaults to True
strip_meta (bool, optional) – if we want to strip any whitespace from the meta fields before concatenating them. Defaults to True
-
-
class
pyqstrat.pyqstrat_cpp.
TextQuoteParser
¶ Bases:
pyqstrat.pyqstrat_cpp.RecordFieldParser
Helper class that parses a quote from a list of fields (strings)
-
__call__
()¶ - Parameters
fields (list of str) – A list of fields representing the record
- Returns
Or None if this field is not a quote
- Return type
-
__init__
()¶ - Parameters
is_quote – a function that takes a list of strings as input and returns a bool if the fields represent a quote
base_date (int) – if the timestamp in the files does not have a date component, pass in the date as number of millis or micros since the epoch
timestamp_indices (list of int) – index of the timestamp field within the record
bid_offer_idx (int) – index of the field that contains whether this is a bid or offer quote
price_idx (int) – index of the price field
qty_idx (int) – index of the quote size field
id_field_indices (list of str) – indices of the fields identifying an instrument. For example, for a future this could be symbol and expiry. These fields will be concatenated with a separator and placed in the id field in the record
meta_field_indices (list of str) – indices of additional fields you want to store. For example, the exchange.
timestamp_parsers – a vector of functions that take a timestamp as a string and returns number of millis or micros since the epoch
bid_str (str) – if the field indicated in bid_offer_idx matches this string, we consider this quote to be a bid
offer_str (str) – if the field indicated in bid_offer_idx matches this string, we consider this quote to be an offer
price_multiplier – (float, optional): sometimes the price in a file could be in hundredths of cents, and we divide by this to get dollars. Defaults to 1.0
strip_id (bool, optional) – if we want to strip any whitespace from the id fields before concatenating them. Defaults to True
strip_meta (bool, optional) – if we want to strip any whitespace from the meta fields before concatenating them. Defaults to True
-
-
class
pyqstrat.pyqstrat_cpp.
TextRecordParser
¶ Bases:
pyqstrat.pyqstrat_cpp.RecordParser
A helper class that takes in a text line, separates it into a list of fields based on a delimiter, and then uess the parsers passed in to try and parse the line as a quote, trade, open interest record or any other type of record
-
__init__
()¶ - Parameters
parsers – A vector of functions that each take a list of strings as input and returns a subclass of
Record
or Noneexclusive (bool, optional) – Set this when each line can only contain one type of record, after one first parser returns a non None object, we will not call other parsers. Default false
separator (str, optional) – A single character string. This is the delimiter we use to separate fields from the text passed in. Default ,
-
-
class
pyqstrat.pyqstrat_cpp.
TextTradeParser
¶ Bases:
pyqstrat.pyqstrat_cpp.RecordFieldParser
Helper class that parses a trade from a list of fields (strings)
-
__call__
()¶ - Parameters
fields (list of str) – A list of fields representing the record
- Returns
or None if this record is not a trade
- Return type
-
__init__
()¶ - Parameters
is_trade – A function that takes a list of strings as input and returns a bool if the fields represent a trade
base_date (int) – If the timestamp in the files does not have a date component, pass in the date as number of millis or micros since the epoch
timestamp_indices (list of int) – Index of the timestamp fields within the record. For example, date and time could be in different fields. We add the result of each timestamp field to get the final timestamp
price_idx (int) – Index of the price field
qty_idx (int) – Index of the quote size field
id_field_indices (list of str) – Indices of the fields identifying an instrument. For example, for a future this could be symbol and expiry. These fields will be concatenated with a separator and placed in the id field in the record
meta_field_indices (list of str) – Indices of additional fields you want to store. For example, the exchange.
timestamp_parsers – A list of functions that takes a timestamp as a string and returns number of millis or micros since the epoch
price_multiplier – (float, optional): Sometimes the price in a file could be in hundredths of cents, and we divide by this to get dollars. Defaults to 1.0
strip_id (bool, optional) – If we want to strip any whitespace from the id fields before concatenating them. Defaults to True
strip_meta (bool, optional) – If we want to strip any whitespace from the meta fields before concatenating them. Defaults to True
-
-
class
pyqstrat.pyqstrat_cpp.
TimestampParser
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
class
pyqstrat.pyqstrat_cpp.
TradeBarAggregator
¶ Bases:
pyqstrat.pyqstrat_cpp.Aggregator
- Aggregate trade records to create trade bars, given a frequency. Calculates open, high, low, close, volume, vwap as well as last_update_time
which is timestamp of the last trade that we processed before the bar ended.
-
__call__
()¶ Add a trade record to be written to disk at some point
- Parameters
trade (
TradeRecord
) –line_number (int) – The line number of the source file that this trade came from. Used for debugging
-
__init__
()¶ - Parameters
writer_creator – A function that takes an output_file_prefix, schema, create_batch_id and max_batch_size and returns an object implementing the
Writer
interfaceoutput_file_prefix (str) – Path of the output file to create. The writer and aggregator may add suffixes to this to indicate the kind of data and format the file creates. E.g. “/tmp/output_file_1”
frequency (str, optional) – A string like “5m” indicating the bar size is 5 mins. Units can be s,m,h or d for second, minute, hour or day. Defaults to “5m”
batch_by_id (bool, optional) – If set, we will create one batch for each id. This will allow us to retrieve all records for a single instrument by reading a single batch. Defaults to True.
batch_size (int, optional) – If set, we will write a batch to disk every time we have this many records queued up. Defaults to 2.1 billion
timestamp_unit (Schema.Type, optional) – Whether timestamps are measured as milliseconds or microseconds since the unix epoch. Defaults to Schema.TIMESTAMP_MILLI
-
close
()¶ Flush all unwritten records to the Writer, which writes them to disk when its close function is called
-
class
pyqstrat.pyqstrat_cpp.
TradeRecord
¶ Bases:
pyqstrat.pyqstrat_cpp.Record
A parsed trade record that we can save to disk
-
id
¶ A unique string representing a symbol or instrument id
- Type
str
-
timestamp
¶ Trade time, in milliseconds or microseconds since 1/1/1970
- Type
int
-
qty
¶ Trade quantity
- Type
float
-
price
¶ Trade price
- Type
float
-
metadata
¶ a string representing any extra information you want to save, such as exchange, or special trade conditions
- Type
str
-
__init__
()¶
-
id
-
metadata
-
price
-
qty
-
timestamp
-
-
class
pyqstrat.pyqstrat_cpp.
Writer
¶ Bases:
pybind11_builtins.pybind11_object
An abstract class that you subclass to provide an object that can write to disk
-
__init__
¶ Initialize self. See help(type(self)) for accurate signature.
-
close
()¶ Close the writer and flush any remaining data to disk
- Parameters
success (bool, optional) – If set to False, we had some kind of exception and are cleaning up. Tells the function to not indicate the file was written successfully, for example by renaming a temp file to the actual filename. Defaults to True
-
write_batch
()¶ Write a batch of records to disk. The batch can have an optional string id so we can later retrieve just this batch of records without reading the whole file
- Parameters
batch_id (str, optional) – An identifier which can later be used to retrieve this batch from disk. Defaults to “”
-
-
class
pyqstrat.pyqstrat_cpp.
WriterCreator
¶ Bases:
pybind11_builtins.pybind11_object
-
__call__
()¶
-
__init__
()¶
-
-
pyqstrat.pyqstrat_cpp.
black_scholes_price
(call: numpy.ndarray[bool], S: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], sigma: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ Compute Euroepean option price :param call: True for a call option, False for a put :type call: bool :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1%. Default 0 :type q: float, optional
- Returns
Option price
- Return type
float
-
pyqstrat.pyqstrat_cpp.
cdf
(x: numpy.ndarray[float64]) → object¶ Cumulative density function of normal distribution :param x: random variable :type x: float
- Returns
cdf of the random variable
- Return type
float
-
pyqstrat.pyqstrat_cpp.
d1
(S: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], sigma: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ d1 from Black Scholes :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1% :type q: float
- Returns
- Return type
float
-
pyqstrat.pyqstrat_cpp.
d2
(S: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], sigma: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ d2 from Black Scholes :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1% :type q: float
- Returns
- Return type
float
-
pyqstrat.pyqstrat_cpp.
delta
(call: numpy.ndarray[bool], S: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], sigma: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ Compute European option delta :param call: True for a call option, False for a put :type call: bool :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1%. Default 0 :type q: float, optional
- Returns
Option delta
- Return type
float
-
pyqstrat.pyqstrat_cpp.
gamma
(S: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], sigma: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ Compute European option gamma. :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1%. Default 0 :type q: float, optional
- Returns
Option gamma
- Return type
float
-
pyqstrat.pyqstrat_cpp.
implied_vol
(call: numpy.ndarray[bool], price: numpy.ndarray[float64], S: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ Compute implied volatility for a European option. :param call: True for a call option, False for a put :type call: bool :param price: The option premium :type price: float :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param q: Annualized dividend yield. Use 0.01 for 1%. Default 0 :type q: float, optional
- Returns
Implied volatility. For 1% we return 0.01
- Return type
float
-
class
pyqstrat.pyqstrat_cpp.
ostream_redirect
¶ Bases:
pybind11_builtins.pybind11_object
-
__init__
(self: pyqstrat.pyqstrat_cpp.ostream_redirect, stdout: bool=True, stderr: bool=True) → None¶
-
-
pyqstrat.pyqstrat_cpp.
rho
(call: numpy.ndarray[bool], S: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], sigma: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ Compute European option rho. This is Black Scholes formula rho divided by 100 so we get rho per 1% change in interest rate :param call: True for a European call option, False for a put :type call: bool :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1%. Default 0 :type q: float, optional
- Returns
Option theta
- Return type
float
-
pyqstrat.pyqstrat_cpp.
theta
(call: numpy.ndarray[bool], F: numpy.ndarray[float64], K: numpy.ndarray[float64], t: numpy.ndarray[float64], r: numpy.ndarray[float64], sigma: numpy.ndarray[float64], q: numpy.ndarray[float64]=0.0) → object¶ Compute European option theta per day. This is Black Scholes formula theta divided by 365 to give us the customary theta per day :param call: True for a call option, False for a put :type call: bool :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1%. Default 0 :type q: float, optional
- Returns
Option theta
- Return type
float
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pyqstrat.pyqstrat_cpp.
vega
(S: float, K: float, t: float, r: float, sigma: float, q: float=0.0) → float¶ Compute European option vega. This is Black Scholes formula vega divided by 100 so we get rho per 1% change in interest rate :param S: Spot price. For a future discount the future price using exp(-rt) :type S: float :param K: Strike :type K: float :param t: Time to maturity in years :type t: float :param r: Continuously compounded interest rate. Use 0.01 for 1% :type r: float :param sigma: Annualized volatility. Use 0.01 for 1% :type sigma: float :param q: Annualized dividend yield. Use 0.01 for 1%. Default 0 :type q: float, optional
- Returns
Option vega
- Return type
float
pyqstrat.marketdata_processor module¶
-
class
pyqstrat.marketdata_processor.
PathFileNameProvider
(path, include_pattern=None, exclude_pattern=None)[source]¶ Bases:
object
A helper class that, given a pattern such as such as “/tmp/abc*.gz” and an optional include and exclude pattern, returns names of all files that match
-
__init__
(path, include_pattern=None, exclude_pattern=None)[source]¶ - Parameters
path (str) – A pattern such as “/tmp/abc*.gz”
include_pattern (str) – Given a pattern such as “xzy”, will return only filenames that contain xyz
exclude_pattern (str) – Given a pattern such as “_tmp”, will exclude all filenames containing _tmp
-
-
class
pyqstrat.marketdata_processor.
SingleDirectoryFileNameMapper
(output_dir)[source]¶ Bases:
object
A helper class that provides a mapping from input filenames to their corresponding output filenames in an output directory.
-
__call__
(input_filepath)[source]¶ - Parameters
input_filepath (str) – The input file that we are creating an output file for, e.g. “/home/xzy.gz”
- Returns
- Output file path for that input. We take the filename from the input filepath, strip out any extension
and prepend the output directory name
- Return type
str
-
-
class
pyqstrat.marketdata_processor.
TextHeaderParser
(record_generator, skip_rows=0, separator=', ', make_lowercase=True)[source]¶ Bases:
object
Parses column headers from a text file containing market data
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__call__
(input_filename, compression)[source]¶ Args:
input_filename (str): The file to read compression (str): Compression type, e.g. “gzip”, or None if the file is not compressed
- Returns
column headers
- Return type
list of str
-
__init__
(record_generator, skip_rows=0, separator=', ', make_lowercase=True)[source]¶ - Parameters
record_generator – A function that takes a filename and its compression type and returns an object that we can use to iterate through lines in that file
skip_rows (int, optional) – Number of rows to skip before starting to read the file. Default is 0
separator (str, optional) – Separator for headers. Defaults to ,
make_lowercase (bool, optional) – Whether to convert headers to lowercase before returning them
-
-
pyqstrat.marketdata_processor.
base_date_filename_mapper
(input_file_path)[source]¶ A helper function that parses out the date from a filename. For example, given a file such as “/tmp/spx_2018-08-09”, this parses out the date part of the filename and returns milliseconds (no fractions) since the epoch to that date.
- Parameters
input_filepath (str) – Full path to the input file
- Returns
Milliseconds since unix epoch to the date implied by that file
- Return type
int
>>> base_date_filename_mapper("/tmp/spy_1970-1-2_quotes.gz") 86400000
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pyqstrat.marketdata_processor.
create_text_file_processor
(record_generator, line_filter, record_parser, bad_line_handler, record_filter, missing_data_handler, aggregators, skip_rows=1)[source]¶
-
pyqstrat.marketdata_processor.
get_field_indices
(field_names, headers)[source]¶ Helper function to get indices of field names in a list of headers
- Parameters
field_names (list of str) – The fields we want indices of
headers (list of str) – All headers
- Returns
indices of each field name in the headers list
- Return type
list of int
-
pyqstrat.marketdata_processor.
process_marketdata
(input_filename_provider, file_processor, num_processes=None, raise_on_error=True)[source]¶ Top level function to process a set of market data files
- Parameters
input_filename_provider – A function that returns a list of filenames (incl path) we need to process.
file_processor – A function that takes an input filename and processes it, returning number of lines processed.
num_processes (int, optional) – The number of processes to run to parse these files. If set to None, we use the number of cores present on your machine. Defaults to None
raise_on_error (bool, optional) – If set, we raise an exception when there is a problem with parsing a file, so we can see a stack trace and diagnose the problem. If not set, we print the error and continue. Defaults to True
-
pyqstrat.marketdata_processor.
process_marketdata_file
(input_filename, output_file_prefix_mapper, record_parser_creator, aggregator_creator, line_filter=None, compression=None, base_date_mapper=None, file_processor_creator=<function create_text_file_processor>, header_parser_creator=<function <lambda>>, header_record_generator=<function text_file_record_generator>, record_generator=<pyqstrat.pyqstrat_cpp.TextFileDecompressor object>, bad_line_handler=<pyqstrat.pyqstrat_cpp.PrintBadLineHandler object>, record_filter=None, missing_data_handler=<pyqstrat.pyqstrat_cpp.PriceQtyMissingDataHandler object>, writer_creator=<pyqstrat.pyqstrat_cpp.ArrowWriterCreator object>)[source]¶ Processes a single market data file
- Parameters
input_filename (str) –
output_file_prefix_mapper – A function that takes an input filename and returns the corresponding output filename we want
record_parser_creator – A function that takes a date and a list of column names and returns a function that can take a list of fields and return a subclass of Record
line_filter (optional) – A function that takes a line and decides whether we want to keep it or discard it. Defaults to None
compression (str, optional) – Compression type for the input file. Defaults to None
base_date_mapper (optional) – A function that takes an input filename and returns the date implied by the filename, represented as millis since epoch. Defaults to helper
function base_date_filename_mapper
file_processor_creator (optional) – A function that returns an object that we can use to iterate through lines in a file. Defaults to helper function
create_text_file_processor
bad_line_handler (optional) – A function that takes a line that we could not parse, and either parses it or does something else like recording debugging info, or stopping the processing by raising an exception. Defaults to helper function
PrintBadLineHandler
record_filter (optional) – A function that takes a parsed TradeRecord, QuoteRecord, OpenInterestRecord or OtherRecord and decides whether we want to keep it or discard it. Defaults to None
missing_data_handler (optional) – A function that takes a parsed TradeRecord, QuoteRecord, OpenInterestRecord or OtherRecord, and decides deals with any data that is missing in those records. For example, 0 for bid could be replaced by NAN. Defaults to helper function:
price_qty_missing_data_handler
writer_creator (optional) – A function that takes an output_file_prefix, schema, whether to create a batch id file, and batch_size and returns a subclass of
Writer
. Defaults to helper function:arrow_writer_creator
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pyqstrat.marketdata_processor.
text_file_record_generator
(filename, compression)[source]¶ A helper function that returns an object that we can use to iterate through lines in the input file :param filename: The input filename :type filename: str :param compression: The compression type of the input file or None if its not compressed :type compression: str
pyqstrat.trade_bars module¶
-
class
pyqstrat.trade_bars.
TradeBars
(timestamps, c, o=None, h=None, l=None, v=None, vwap=None)[source]¶ Bases:
object
Used to store OHLCV bars. You must at least supply timestamps and close prices. All other fields are optional.
-
timestamp
¶ A numpy datetime array with the datetime for each bar. Must be monotonically increasing.
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c
¶ A numpy float array with close prices for the bar.
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o
¶ A numpy float array with open prices . Default None
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h
¶ A numpy float array with high prices. Default None
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l
¶ A numpy float array with high prices. Default None
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v
¶ A numpy integer array with volume for the bar. Default None
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vwap
¶ A numpy float array with the volume weighted average price for the bar. Default None
-
__init__
(timestamps, c, o=None, h=None, l=None, v=None, vwap=None)[source]¶ Zeroes in o, h, l, c are set to nan
-
add_timestamps
(timestamps)[source]¶ Adds new timestamps to a market data object.
- Parameters
timestamps (np.array of np.datetime64) – New timestamps to add. Does not have to be sorted or unique
>>> timestamps = np.array(['2018-01-05', '2018-01-09', '2018-01-10'], dtype = 'M8[ns]') >>> c = np.array([8.1, 8.2, 8.3]) >>> o = np.array([9, 10, 11]) >>> trade_bar = TradeBars(timestamps, c, o) >>> new_timestamps = np.array(['2018-01-07', '2018-01-09'], dtype = 'M8[ns]') >>> trade_bar.add_timestamps(new_timestamps) >>> print(trade_bar.timestamps) ['2018-01-05T00:00:00.000000000' '2018-01-07T00:00:00.000000000' '2018-01-09T00:00:00.000000000' '2018-01-10T00:00:00.000000000'] >>> np.set_printoptions(formatter = {'float' : lambda x : f'{x:.4f}'}) # After numpy 1.13 positive floats don't have a leading space for sign >>> print(trade_bar.o, trade_bar.c) [9.0000 nan 10.0000 11.0000] [8.1000 nan 8.2000 8.3000]
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describe
(warn_std=10, time_distribution_frequency='15 min', print_time_distribution=False)[source]¶ Describe the bars. Shows an overview, errors and warnings for the bar data. This is a good function to use before running any backtests on a set of bar data.
- Parameters
warn_std – See warning function
time_distribution_frequency – See time_distribution function
print_time_distribution – Whether to print the time distribution in addition to plotting it.
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errors
(display=True)[source]¶ Returns a dataframe indicating any highs that are lower than opens, closes, lows or lows that are higher than other columns Also includes any ohlcv values that are negative
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overview
(display=True)[source]¶ Returns a dataframe showing basic information about the data, including count, number and percent missing, min, max
- Parameters
display – Whether to print out the warning dataframe as well as returning it
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plot
(figsize=(15, 8), date_range=None, sampling_frequency=None, title='Price / Volume')[source]¶ Plot a candlestick or line plot depending on whether we have ohlc data or just close prices
- Parameters
figsize – Size of the figure (default (15,8))
date_range – A tuple of strings or numpy datetimes for plotting a smaller sample of the data, e.g. (“2018-01-01”, “2018-01-06”)
sampling_frequency – Downsample before plotting. See pandas frequency strings for possible values.
title – Title of the graph, default “Price / Volume”
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resample
(sampling_frequency)[source]¶ Downsample the trade bars data into a new bar frequency
- Parameters
sampling_frequency – See sampling frequency in pandas
inplace – If set to False, don’t modify this object, return a new object instead.
-
time_distribution
(frequency='15 minutes', display=True, plot=True, figsize=None)[source]¶ Return a dataframe with the time distribution of the bars
- Parameters
frequency – The width of each bin (default “15 minutes”). You can use hours or days as well.
display – Whether to display the data in addition to returning it.
plot – Whether to plot the data in addition to returning it.
figsize – If plot is set, optional figure size for the plot (default (20,8))
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warnings
(warn_std=10, display=True)[source]¶ Returns a dataframe indicating any values where the bar over bar change is more than warn_std standard deviations.
- Parameters
warn_std – Number of standard deviations to use as a threshold (default 10)
display – Whether to print out the warning dataframe as well as returning it
-
-
pyqstrat.trade_bars.
roll_futures
(fut_prices, date_func, condition_func, expiries=None, return_full_df=False)[source]¶ Construct a continuous futures dataframe with one row per datetime given rolling logic
- Parameters
md – A dataframe containing the columns ‘date’, ‘series’, and any other market data, for example, ohlcv data. Date can contain time for sub-daily bars. The series column must contain a different string name for each futures series, e.g. SEP2018, DEC2018, etc.
date_func – A function that takes the market data object as an input and returns a numpy array of booleans True indicates that the future should be rolled on this date if the condition specified in condition_func is met. This function can assume that we have all the columns in the original market data object plus the same columns suffixed with _next for the potential series to roll over to.
condition_func – A function that takes the market data object as input and returns a numpy array of booleans. True indicates that we should try to roll the future at that row.
expiries – An optional dataframe with 2 columns, ‘series’ and ‘expiry’. This should have one row per future series indicating that future’s expiry date. If you don’t pass this in, the function will assume that the expiry column is present in the original dataframe.
return_full_df – If set, will return the datframe without removing extra timestamps so you can use your own logic for rolling, including the _next columns and the roll flag
- Returns
- A pandas DataFrame with one row per date, which contains the columns in the original md DataFrame and the same columns suffixed with _next
representing the series we want to roll to. There is also a column called roll_flag which is set to True whenever the date and roll condition functions are met.
>>> fut_prices = pd.DataFrame({'timestamp' : np.concatenate((np.arange(np.datetime64('2018-03-11'), np.datetime64('2018-03-16')), ... np.arange(np.datetime64('2018-03-11'), np.datetime64('2018-03-16')))), ... 'c' : [10, 10.1, 10.2, 10.3, 10.4] + [10.35, 10.45, 10.55, 10.65, 10.75], ... 'v' : [200, 200, 150, 100, 100] + [100, 50, 200, 250, 300], ... 'series' : ['MAR2018'] * 5 + ['JUN2018'] * 5})[['timestamp','series', 'c', 'v']] >>> expiries = pd.Series(np.array(['2018-03-15', '2018-06-15'], dtype = 'M8[D]'), index = ['MAR2018', 'JUN2018'], name = "expiry") >>> date_func = lambda fut_prices : fut_prices.expiry - fut_prices.timestamp <= np.timedelta64(3, 'D') >>> condition_func = lambda fut_prices : fut_prices.v_next > fut_prices.v >>> df = roll_futures(fut_prices, date_func, condition_func, expiries) >>> print(df[df.series == 'MAR2018'].timestamp.max() == np.datetime64('2018-03-14')) True >>> print(df[df.series == 'JUN2018'].timestamp.max() == np.datetime64('2018-03-15')) True