# Utilities

These utility functions are helpful when writing strategies. The utils module is imported for you when you generate a new strategy but here's the code anyway:

from jesse import utils

Here's a reference for all the methods:

# risk_to_qty

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

TIP

This is probably the most important helper function that you're going to need in your strategies. Those of you whom are familiar with compounding risk would love this function.

We made a website for you just to play with this simple but important formula.

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

Properties:

  • capital: float
  • risk_per_capital: float
  • entry_price: float
  • stop_loss_price: float
  • 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.capital
    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)

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)

Properties:

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

Return Type: float

# 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)

Properties:

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

Return Type: float

# 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)

Properties:

  • qty: float
  • price: float

Return Type: float

# anchor_timeframe

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

anchor_timeframe(timeframe)

Properties:

  • timeframe: str

Return Type: str

Example:

One useful example for this could be in your routes file when you need to define the anchor timeframe. Let's say for example we're trading 4h timeframe but don't know the anchor timeframe for it.









 


from jesse.utils import anchor_timeframe

# trading routes
routes = [
    ('Binance', 'BTCUSDT', '4h', 'ExampleStrategy'),
]

extra_candles = [
    ('Binance', 'BTCUSDT', anchor_timeframe('4h')),
]

# 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)

Properties:

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

Return Type: float

# estimate_risk

Estimates the risk per share

estimate_risk(entry_price, stop_price)

Properties:

  • entry_price: float
  • stop_price: float

Return Type: float

# crossed

Helper for the detection of crosses

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

# 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")

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