Utilities ​

Risk management and statistic calculations are essential for a successful algo trading career.

Hence, Jesse offers a collection of commonly used utility functions that life easier for quants.

anchor_timeframe ​

Returns the anchor timeframe. Useful for writing dynamic strategies using multiple timeframes.

anchor_timeframe(timeframe)

anchor_timeframe(timeframe)

Properties:

• timeframe: str

Return Type: str

Example:

bigger_timeframe = anchor_timeframe('1h') # prints '4h'

bigger_timeframe = anchor_timeframe('1h') # prints '4h'

are_cointegrated ​

Uses unit-root test on residuals to test for a cointegrated relationship between two price return series.

The cutoff parameter points to the p-value threshold used in the formula.

are_cointegrated(
    price_returns_1: np.ndarray, price_returns_2: np.ndarray, cutoff=0.05
) -> bool

are_cointegrated(
    price_returns_1: np.ndarray, price_returns_2: np.ndarray, cutoff=0.05
) -> bool

Properties:

• price_returns_1: np.ndarray
• price_returns_1: np.ndarray
• cutoff: float | default=0.05

Return Type: bool

crossed ​

Helper for the detection of crosses

crossed(series1, series2, direction=None, sequential=False)

crossed(series1, series2, direction=None, sequential=False)

Properties:

• series1: np.ndarray
• series2: float, int, np.ndarray
• direction: str - default: None - above or below

Return Type: bool | np.ndarray

combinations_without_repeat ​

Creates an array containing all combinations of the passed arrays individual values without repetitions. Useful for the optimization mode.

combinations_without_repeat(a: np.ndarray, n: int = 2) -> np.ndarray

combinations_without_repeat(a: np.ndarray, n: int = 2) -> np.ndarray

Properties:

• a: np.ndarray
• n: int - default: 2

Return Type: np.ndarray

estimate_risk ​

Estimates the risk per share

estimate_risk(entry_price, stop_price)

estimate_risk(entry_price, stop_price)

Properties:

• entry_price: float
• stop_price: float

Return Type: float

kelly_criterion ​

Returns the Kelly Criterion.

kelly_criterion(win_rate, ratio_avg_win_loss)

kelly_criterion(win_rate, ratio_avg_win_loss)

Properties:

• win_rate: float
• ratio_avg_win_loss: float

Return Type: float

limit_stop_loss ​

Limits the stop-loss price according to the max allowed risk percentage. (How many percent you're OK with the price going against your position)

limit_stop_loss(entry_price, stop_price, trade_type, max_allowed_risk_percentage)

limit_stop_loss(entry_price, stop_price, trade_type, max_allowed_risk_percentage)

Properties:

• entry_price: float
• stop_price: float
• trade_type: str
• max_allowed_risk_percentage: float

Return Type: float

numpy_candles_to_dataframe ​

Helper to convert numpy to financial dataframe

numpy_candles_to_dataframe(candles: np.ndarray, name_date="date", name_open="open", name_high="high",
                               name_low="low", name_close="close", name_volume="volume")

numpy_candles_to_dataframe(candles: np.ndarray, name_date="date", name_open="open", name_high="high",
                               name_low="low", name_close="close", name_volume="volume")

Properties:

• candles: np.ndarray
• name_date: str
• name_open: str
• name_high: str
• name_low: str
• name_close: str
• name_volume: str

Return Type: pd.DataFrame

qty_to_size ​

Converts a quantity to its corresponding position-size. Example: Requesting 2 shares at the price of $50 would return $100.

qty_to_size(qty, price)

qty_to_size(qty, price)

Properties:

• qty: float
• price: float

Return Type: float

risk_to_qty ​

Calculates the quantity, based on the percentage of the capital you're willing to risk per trade.

risk_to_qty(capital, risk_per_capital, entry_price, stop_loss_price, precision=3, fee_rate=0)

risk_to_qty(capital, risk_per_capital, entry_price, stop_loss_price, precision=3, fee_rate=0)

Properties:

• capital: float
• risk_per_capital: float
• entry_price: float
• stop_loss_price: float
• precision: int - default: 3
• fee_rate: float - default: 0

Return Type: float

Example:

def go_long(self):
    # risk 1% of the capital($10000) for a trade entering at $100 with the stop-loss at $80
    risk_perc = 1
    entry = 100
    stop = 80
    profit = 150
    capital = 10000
    # or we could access capital dynamically:
    capital = self.balance
    qty = utils.risk_to_qty(capital, risk_perc, entry, stop)

    self.buy = qty, entry
    self.stop_loss = qty, stop
    self.take_profit = qty, profit

def go_long(self):
    # risk 1% of the capital($10000) for a trade entering at $100 with the stop-loss at $80
    risk_perc = 1
    entry = 100
    stop = 80
    profit = 150
    capital = 10000
    # or we could access capital dynamically:
    capital = self.balance
    qty = utils.risk_to_qty(capital, risk_perc, entry, stop)

    self.buy = qty, entry
    self.stop_loss = qty, stop
    self.take_profit = qty, profit

In real trading, you usually need to include the exchange fee in qty calculation to make sure you don't spend more than the existing capital (in which case Jesse would raise an error):

# so instead of 
qty = utils.risk_to_qty(capital, risk_perc, entry, stop)

# it's better to do
qty = utils.risk_to_qty(capital, risk_perc, entry, stop, self.fee_rate)

# so instead of 
qty = utils.risk_to_qty(capital, risk_perc, entry, stop)

# it's better to do
qty = utils.risk_to_qty(capital, risk_perc, entry, stop, self.fee_rate)

See Also: fee_rate

risk_to_size ​

Calculates the size of the position based on the amount of risk percentage you're willing to take.

risk_to_size(capital_size, risk_percentage, risk_per_qty, entry_price)

risk_to_size(capital_size, risk_percentage, risk_per_qty, entry_price)

Properties:

• capital_size: float
• risk_percentage: float
• risk_per_qty: float
• entry_price: float

Return Type: float

signal_line ​

Returns the moving average of the series. Useful to create so called signal lines of indicators.

signal_line(series, period=10, matype=0)

signal_line(series, period=10, matype=0)

Properties:

• series: np.array
• period: int - default = 10
• matype: int - default = 0

See here for available matypes

Return Type: np.array

size_to_qty ​

Converts a position-size to the corresponding quantity. Example: Requesting $100 at the price of $50 would return 2.

size_to_qty(position_size, price, precision=3, fee_rate=0)

size_to_qty(position_size, price, precision=3, fee_rate=0)

Properties:

• price: float
• position_size: float
• precision: int - default: 3
• fee_rate: float - default: 0

Return Type: float

streaks ​

Returns the streaks of the series. A positive number stands for a positive streak and a negativ number for a negative streak. By default it uses the first discrete difference.

streaks(series: np.array, use_diff=True) -> np.array

streaks(series: np.array, use_diff=True) -> np.array

Properties:

• series: np.array
• use_diff: bool

Return Type: np.array[bool]

strictly_decreasing ​

Returns whether a series in strictly decreasing or not.

strictly_increasing(series, lookback)

strictly_increasing(series, lookback)

Properties:

• series: np.array
• lookback: int

Return Type: bool

strictly_increasing ​

Returns whether a series in strictly increasing or not.

strictly_increasing(series, lookback)

strictly_increasing(series, lookback)

Properties:

• series: np.array
• lookback: int

Return Type: bool

subtract_floats ​

Subtracts two floats without the rounding issue in Python

subtract_floats(float1: float, float2: float) -> float

subtract_floats(float1: float, float2: float) -> float

Properties:

• float1: float
• float2: float

Return Type: float

sum_floats ​

Sums two floats without the rounding issue in Python

sum_floats(float1: float, float2: float) -> float

sum_floats(float1: float, float2: float) -> float

Properties:

• float1: float
• float2: float

Return Type: float

prices_to_returns ​

Converts a series of asset prices to returns.

In case you're wondering why you need to use price returns for price series analysis instead of the price values, check out this answer on Quant Stackexchange.

prices_to_returns(price_series: np.ndarray) -> np.ndarray

prices_to_returns(price_series: np.ndarray) -> np.ndarray

Properties:

• price_series: np.ndarray

Return Type: np.ndarray

wavelet_denoising ​

Denoises / filters timeseries data. First deconstructs and then reconstructs based on a threshold.

wavelet_denoising(raw: np.ndarray, wavelet: str ='haar', level: int = 1, mode: str = 'symmetric', smoothing_factor: float = 0, threshold_mode: str = 'hard') -> np.ndarray

wavelet_denoising(raw: np.ndarray, wavelet: str ='haar', level: int = 1, mode: str = 'symmetric', smoothing_factor: float = 0, threshold_mode: str = 'hard') -> np.ndarray

Properties:

• raw: np.ndarray
• wavelet: str
• level: int
• mode: str
• smoothing_factor: float
• threshold_mode: str

Return Type: np.ndarray

z_score ​

A Z-score is a numerical measurement that describes how many standard deviations far away the data is comparing to the mean of the data.

z_score(price_returns: np.ndarray) -> np.ndarray

z_score(price_returns: np.ndarray) -> np.ndarray

Properties:

• price_returns: np.ndarray

Return Type: np.ndarray