quantpylib.simulator.gene

quantpylib.simulator.gene houses powerful features for numerical computations involving market and non-market variables, including a no-code mathematical parser-evaluator that computes trading signals/indicators from formulaic, well-defined Python str objects. The parser-evaluator is exposed via the quantpylib.simulator.gene.Gene class APIs, which internally uses a tree-data structure to encode trading formulas. The quantpylib.simulator.gene.GeneticAlpha class leverages this parser-evaluator, as well as the backtest engine provided by our quantpylib.simulator.alpha.Alpha class to provide a no-code solution to backtesting trading strategies. The GeneticAlpha class extends the Alpha class to implement all the necessary methods for signal computation, forecast generation, position sizing, risk-management, volatility targeting and backtest logic. All the performance metrics and hypothesis testing suites made available to the Alpha objects are naturally available to any GeneticAlpha instance via the same function signatures.

GeneticAlpha

(Bases: quantpylib.simulator.alpha.Alpha )

Parameters:

Name	Type	Description	Default
`genome`	`Gene or str`	Genome representation as `Gene` object or in mathematical string format.	required
`**kwargs`		Backtest parameters required to instantiate `quantpylib.simulator.alpha.Alpha` objects.	`{}`

Parameters in **kwargs are required and passed into quantpylib.simulator.alpha.Alpha and are as follows:

Parameters:

Name	Type	Description	Default
`start`	`datetime`	Start of backtest simulation. If not tz-aware, assumed UTC. If not given, assume min of OHLC dataset in dfs.	`None`
`end`	`datetime`	Start of backtest simulation. If not tz-aware, assumed UTC. If not given, assume max of OHLC dataset in dfs.	`None`
`dfs`	`dict`	inst : OHLCV/other Dataframes used for computations. Default is an empty dictionary.	`{}`
`instruments`	`list`	List of traded instruments.	`[]`
`execrates`	`ndarray`	Execution rates for each instrument. Default is None.	`None`
`commrates`	`ndarray`	Commission rates for each instrument. Default is None.	`None`
`longswps`	`ndarray`	Long annualized swap/funding rates for each instrument. Positive swaps means long positions incur swap fees. Default is None.	`None`
`shortswps`	`ndarray`	Short annualized swap/funding rates for each instrument. Positive swaps means short positions incur swap fees. Default is None.	`None`
`granularity`	`Period`	The granularity of each trading signal evaluation. Datapoints of lower granularity than specified are ignored. Last known datapoint of multiple entries in the same granularity interval is taken. Default is `Period.DAILY`.	`DAILY`
`positional_inertia`	`float`	Parameter controlling position change inertia. Default is 0.	`0`
`portfolio_vol`	`float`	Target portfolio volatility. Default is 0.20, representing 20% annualized volatility.	`0.2`
`weekend_trading`	`bool`	Indicates if there is weekend trading, such as in cryptocurrency markets. Defaults to False.	`False`
`around_the_clock`	`bool`	Indicates if there is 24H trading, such as in cryptocurrency and fx markets. Defaults to False.	`False`
`currency_denomination`	`str`	Currency denomination for the portfolio. Default is "USD".	`'USD'`
`starting_capital`	`float`	Amount to begins the backtest with. Defaults to 10000.0.	`10000.0`

Notes: execrates, commrates, longswps, shortswps are presented in decimals. execrates = [0.001, 0.005, ...] encodes that 0.1% of notional value transacted is deducted as execution costs for first instrument, second instrument... commrates specify commisions in the same units (as percentage of notional value), as for overnight swap rates for both long and short positions.

`run_simulation` `async`

Runs the entire backtest.

Parameters:

Name	Type	Description	Default
`verbose`		(boolean, optional) flag to print out backtest simulation information at runtime.	`False`

Returns:

Type	Description
`DataFrame`	a DataFrame containing backtest statistics. Contains information about contracts held throughtout the backtest, portfolio weights, portfolio leverage, nominal exposusure, execution costs, commissions, swaps, PnL, portfolio capital and so on.

`get_performance_measures`

Computes the performance metrics for the trading strategy.

Returns:

Type Description

dict

A dictionary containing various performance metrics:

"cum_ret": Cumulative returns over time.
"log_ret": Logarithmic returns.
"max_dd": Maximum drawdown.
"1y_roll_dd": One-year rolling drawdown.
"1y_roll_max_dd": One-year rolling maximum drawdown.
"sortino": Sortino ratio.
"sharpe": Sharpe ratio.
"mean_ret": Mean return per annum.
"median_ret": Median return per annum.
"stdev_ret": Standard deviation of returns per annum.
"var_ret": Variance of returns per annum.
"skew_ret": Skewness of returns.
"kurt_exc": Excess kurtosis of returns.
"cagr": Compound annual growth rate.
"3y_roll_cagr": Three-year rolling compound annual growth rate.
"3y_roll_calmar": Three-year rolling Calmar ratio.
"omega(0)": Omega ratio.
"ulcer": Ulcer index.
"VaR95": Value at Risk at 95% confidence level.
"cVaR95": Conditional Value at Risk at 95% confidence level.
"gain_to_pain": Gain-to-pain ratio.
"w_summary": Summary statistics of weights.
"directionality": Market long bias directionality.
"parity_distance": Distance from a 1/n equal weight portfolio.

`hypothesis_tests` `async`

Conducts monte carlo permutation p-value hypothesis tests on the performance of the trading strategy represented by the object instance.

Parameters:

Name	Type	Description	Default
`num_decision_shuffles`	`int`	Number of decision shuffles for monto carlo permutation tests. Default is 1000.	`1000`
`num_data_shuffles`	`int`	Number of data shuffles for permutation tests. Default is 10 (computationally expensive).	`10`

Returns:

Type Description

dict

A dictionary containing the results of hypothesis tests.

'timer_p': p-value from asset-timing test that shuffles time-series asset returns.
'picker_p': p-value from asset-picking shuffler test that shuffles cross-sectional asset returns.
'trader_p1': p-value from decision-making test that shuffles both time-series and cross-sectional asset returns.
'trader_p2': p-value from decision-making test that shuffles market data.

`Gene`

Represents a formulaic alpha expression used to encode trading rules.

This class internally represents a trading rule as a tree data structure, where each node can either be a terminal (leaf) node or a functional node. Terminal nodes represent data points or constants, while functional nodes represent operations on their child nodes.

`str_to_gene` `staticmethod`

Converts a string representation of a gene/formulaic alpha into a Gene object.

Parameters:

Name	Type	Description	Default
`strgene`	`str`	The string representation of the gene.	required

Returns:

Type	Description
`Gene`	A Gene object representing the gene.

Raises:

Type	Description
`AssertionError`	If strgene representation is misspecified by syntax rules.

Notes

This is the recommended and most intuitive way of creating a Gene object.

`init`

Initializes a Gene object.

Parameters:

Name	Type	Description	Default
`prim`	`str`	The primary function/terminal of the gene.	required
`space`	`str`	The space value associated with the gene, specifying details of the primitive. For example, in the context of financial trading, this could represent parameters such as window size or lookback period of a rolling correlation function. Defaults to None.	`None`
`is_terminal`	`bool`	Indicates whether the gene is terminal node.	required
`parent`	`Gene`	The parent gene. Defaults to None.	`None`
`children`	`list`	The list of child genes. Defaults to an empty list.	`[]`

The list of prim primitives supported by our library, their behavior and their interpretations can be found here.

`evaluate_node`

Recursively evaluates a node in the formulaic alpha expression. When called on the root node in the gene representation, this function evaluates the entire formulaic expression.

Parameters:

Name	Type	Description	Default
`insts`	`list`	The list of instrument names.	required
`dfs`	`dict`	A dictionary containing pricing/alternative data DataFrames for each instrument.	required
`idx`	`Index`	The index for alignment.	required

Returns:

Type	Description
`DataFrame`	The evaluated node with DataFrame.index=idx and DataFrame.columns=insts.

dfs should contain DataFrames for all terminals required in the evaluation of the gene representation. For OHLCV terminals, dfs should contain key-value pair of (ticker:pd.DataFrame). pd.DataFrame objects provided should have timezone aware DatetimeIndex properties. For instance:

{
    'BRKB':
                                open      high       low        close        adj_close volume
    datetime
    2000-01-03 00:00:00+00:00  1825.0000  1829.0000  1741.0000  1765.0000    35.3000   873500     
    2000-01-04 00:00:00+00:00  1725.0000  1733.0000  1695.0000  1704.0000    34.0800  1380000     
    2000-01-05 00:00:00+00:00  1707.0000  1773.0000  1695.0000  1732.0000    34.6400   997000     
    2000-01-06 00:00:00+00:00  1745.0000  1804.0000  1727.0000  1804.0000    36.0800   917000     
    2000-01-07 00:00:00+00:00  1830.0000  1848.0000  1805.0000  1820.0000    36.4000  1001500     
    ...                              ...        ...        ...        ...        ...      ...     
    2009-12-24 00:00:00+00:00  3281.9999  3295.9899  3274.9999  3286.9999    65.7400   607600     
    2009-12-28 00:00:00+00:00  3279.9999  3289.9999  3274.9999  3285.3699    65.7074  1080250     
    2009-12-29 00:00:00+00:00  3284.9999  3289.6899  3269.9999  3279.9999    65.6000  1105300     
    2009-12-30 00:00:00+00:00  3282.9999  3289.6499  3279.9999  3289.6499    65.7930   560350     
    2009-12-31 00:00:00+00:00  3289.9999  3300.9899  3279.9999  3285.9999    65.7200   972900 
}

For non-OHLCV terminals, dfs should be supplemented with key-value pair of (ticker_terminal:pd.Series). pd.Series objects provided should have timezone aware DatetimeIndex properties.

{
    'BRKB_earnings' : pd.Series(
            index=[2000-01-03 00:00:00+00:00, ..., 2009-12-30 00:00:00+00:00],
            data=[...]
    ),
    'BRKB_sentiment' : pd.Series(
            index=[2010-01-03 00:00:00+00:00, ..., 2016-12-30 00:00:00+00:00],
            data=[...]
    )
}

`make_dot`

Generate a DOT language representation of the tree structure rooted at this node.

Returns:

Type	Description
`str`	A string containing the DOT language representation of the tree.

Notes

This method uses pre-order traversal to generate the DOT representation of the tree rooted at the current node. Each node in the tree corresponds to a vertex in the DOT graph, and each edge represents the parent-child relationship between nodes.

The generated DOT string can be rendered into a graphical visualization using graphviz or other tools that support the DOT language.

`height`

Return the maximium distance from current node to any leaf node that is a descendant.

`depth`

Return the distance from current node to the root node.

`size`

Return the number of nodes in the graphical respresentation of the formulaic alpha.

`pre_ord_apply`

Apply a function to each node in the tree using pre-order traversal.

This method traverses the tree in a pre-order fashion, meaning it applies the function to the current node before recursively traversing its children. The function is applied to each node along with any additional keyword arguments provided.

Parameters:

Name	Type	Description	Default
`func`	`callable`	A function to be applied to each node in the tree.	required
`**kwargs`		Additional keyword arguments to be passed to the function.	`{}`

List of Primitives

The Op value = idx_op can be taken to be default as union_idx_op, or when explicitly paired with the un Space value. It takes intersect_idx_op when paired with the ix Space value. Examples would be plus(open,close), plus_un(open,close), plus_ix(open,close). The different operators and their behavior is documented here.

Primitive	Space	Op	Terminal	Args	Meaning	Example
const	int,float	-	Yes	-	represents a constant numerical value of x	const_3.14
open	-	-	Yes	-	open price	open
high	-	-	Yes	-	high price	high
low	-	-	Yes	-	low price	low
close	-	-	Yes	-	close price	close
volume	-	-	Yes	-	trade volume	volume
*	-	-	Yes	-	custom variable	* (e.g. epsEst, sentiment)
abs	-	self_idx_op2	No	1	absolute value	abs(minus(close,open))
neg	-	self_idx_op2	No	1	negation	neg(minus(close,open))
log	-	self_idx_op2	No	1	natural logarithm (replacing inf with NaN)	log(volume)
sign	-	self_idx_op2	No	1	sign function	sign(minus(close,open))
tanh	-	self_idx_op2	No	1	tanh function	tanh(cszscre(logret_1()))
sigmoid	-	self_idx_op2	No	1	sigmoid function	sigmoid(cszscre(logret_1()))
recpcal	-	self_idx_op2	No	1	reciprocal (replacing inf with NaN)	recpcal(close)
pow	int	self_idx_op2	No	1	power function (replacing inf with NaN)	pow_2(close)
csrank	-	all_idx_op	No	1	cross-sectional rank (smallest=1, average draws)	csrank(volume)
cszscre	-	all_idx_op	No	1	cross-sectional Z-score	cszscre(volume)
ls	int,float / int,float	all_idx_op	No	1	-1 for values below 25 percentile and +1 for values above 75 percentile	ls_25/75(volume)
delta	int	self_idx_op	No	1	time-series change in variable over time	delta_1(close)
delay	int	self_idx_op	No	1	time-series delay by specified number of periods	delay_1(close)
forward	int	self_idx_op	No	1	time-series lookahead by specified number of periods	forward_1(close)
sum	int	self_idx_op	No	1	sum of time-series values	sum_5(volume)
prod	int	self_idx_op	No	1	product of time-series values	prod_5(volume)
mean, sma	int	self_idx_op	No	1	simple mean of time-series values	mean_5(volume)
ema, ewma	int	self_idx_op	No	1	exponentially weighted moving average	ewma_5(volume)
median	int	self_idx_op	No	1	median of time-series values	median_5(volume)
std	int	self_idx_op	No	1	standard deviation of time-series values	std_5(volume)
var	int	self_idx_op	No	1	variance of time-series values	var_5(volume)
skew	int	self_idx_op	No	1	skewness of time-series values	skew_5(volume)
kurt	int	self_idx_op	No	1	kurtosis of time-series values	kurt_5(volume)
tsrank	int	self_idx_op	No	1	time-series rank	tsrank_5(volume)
tsmax	int	self_idx_op	No	1	maximum value over time	tsmax_5(volume)
tsmin	int	self_idx_op	No	1	minimum value over time	tsmin_5(volume)
tsargmax	int	self_idx_op	No	1	index of maximum value over time	tsargmax_5(volume)
tsargmin	int	self_idx_op	No	1	index of minimum value over time	tsargmin_5(volume)
tszscre	int	self_idx_op	No	1	time-series Z-score	tszscre_5(volume)
max	-,un,ix	idx_op	No	>=2	maximum over arguments	max_ix(open,close,high)
plus	-,un,ix	idx_op	No	>=2	sum over arguments	plus_un(open,close,high)
minus	-,un,ix	idx_op	No	2	subtraction	minus(high,low)
mult	-,un,ix	idx_op	No	2	multiplication	mult(open,close)
div	-,un,ix	idx_op	No	2	division	div(open,close)
and	-,un,ix	idx_op	No	2	logical AND	and(gt(high,low),lt(high,low))
or	-,un,ix	idx_op	No	2	logical OR	or(gt(high,low),lt(high,low))
eq	-,un,ix	idx_op	No	2	logical EQUALS	eq(gt(high,low),lt(high,low))
gt	-,un,ix	idx_op	No	2	greater-than comparison	gt(open,close)
lt	-,un,ix	idx_op	No	2	less-than comparison	lt(open,close)
ite	-,un,ix	idx_op	No	3	if-then-else operation	ite(or(gt(high,low),lt(high,low)),const_1,const_-1)
cor	int	slow_idx_op	No	2	rolling-correlation	cor_12(volume,close)
kentau	int	slow_idx_op	No	2	rolling-Kendall's tau correlation	kentau_12(volume,close)
cov	int	slow_idx_op	No	2	rolling-covariance	cov_12(volume,close)
dot	int	slow_idx_op	No	2	rolling-dot product	dot_12(volume,close)
wmean, wma	int	slow_idx_op	No	2	weighted moving average	wmean_12(close,weights)
grssret	int	-	Pseudo	0	period gross returns	grssret_12()
logret	int	-	Pseudo	0	period log returns	logret_12()
netret	int	-	Pseudo	0	period net returns (gross returns - 1)	netret_12()
volatility	int	-	Pseudo	0	volatility (standard deviation of log returns)	volatility_12()
rsi	int	-	Pseudo	0	relative strength index indicator	rsi_12()
mvwap	int	-	Pseudo	0	moving volume-weighted average price indicator	mvwap_12()
obv	int	-	Pseudo	0	on-balance volume indicator	obv_12()
atr	int	-	Pseudo	0	average true range indicator	atr_12()
tr	-	-	Pseudo	0	true range indicator	tr()
adx	int	-	Pseudo	0	average directional movement index	adx_12()
addv	int	-	Pseudo	0	average daily dollar volume	addv_12()
mac	int / int	-	No	1	moving average crossover indicator function for fast/slow	mac_20/50
vma, vwma	int	-	No	1	volume weighted moving average	vma_20(close)
vwvar	int	-	No	1	volume weighted variance	vmvar_20(logret_1())
vwstd	int	-	No	1	volume weighted standard deviation	vmstd_20(logret_1())

quantpylib.simulator.gene

GeneticAlpha

run_simulation async

get_performance_measures

hypothesis_tests async

Gene

str_to_gene staticmethod

__init__

evaluate_node

make_dot

height

depth

size

pre_ord_apply

List of Primitives

`run_simulation` `async`

`get_performance_measures`

`hypothesis_tests` `async`

`Gene`

`str_to_gene` `staticmethod`

`init`

`evaluate_node`

`make_dot`

`height`

`depth`

`size`

`pre_ord_apply`