overcome 0.2.0

Overcome is a data transformer to calculate the potential buying and selling earnings from the "high", "low" and "close" trading history values.

Overcome

Given a constant take profit and a stop loss, and a time base sorted data vector with the "high", "low", and "close" values from a stock market product, Overcome calculates the potential earnings in both, buying and selling for every step in the timeline.

Usage

Instantiate the Overcome with the constant value for take profit and stop loss. A value for the precision threshold is required as well. The precision threshold applies on whether a "close" value is close to the take profit or stop loss boundaries.

overcome = Overcome(
        threshold=np.float32(0.00001),
        take_profit=np.float32(0.001),
        stop_loss=np.float32(0.001)
    )

Call apply method with the input and wait for the output. The required input structure is a numpy array with a shape of (LENGTH, 3).

The order of the 3 columns is very strict. The first one is for the "high" values, the second one for the "low" values and the third one for "close" values.

earn_buying, earn_selling = overcome.apply(high_low_close)

It returns a tuple of two arrays with the same number of items than the input, indeed matching the same timeline. The first array is for the earnings from buying positions and the second one for the selling ones.

Opened positions limit

Opening all positions without limiting their number may be pretty risky. Overcome simulation supports to set a predefined positions number limit in each direction, buying and selling. So, for example, if the positions limit is set to 10, when the first 10 positions will be opened in the simulated overcome, the following ones won't open, and then they are not accountable for profit or loss.

Creating an Overcome instance with opened positions limited at 10 for buying and 10 for selling is as follows.

overcome = Overcome(
        threshold=np.float32(0.00001),
        take_profit=np.float32(0.001),
        stop_loss=np.float32(0.001),
        positions_limit=10
    )

Input from a dataframe

Starting with a Dataframe as df from any product historical data, convert the columns into the required input by the following expression.

high_low_close = df[["high", "low", "close"]].to_numpy(dtype=np.float32)

Then apply the calculation and merge the result into the original Dataframe as follows.

(df["earn_buying"], df["earn_selling"]) = overcome.apply(high_low_close)

Example

The following table is an example of dataframe with the new columns for buying and selling earnings already calculated. This table is part of a test suite, so the data is not real, it is just prepared to achieve a scenario with a variety of positive and negative earnings in both operations, buying and selling.

The configuration for take profit for this example is 0.001 and the stop loss is 0.0007.

timestamp	close	high	low	earn_buying	earn_selling
2022-04-01 00:01:00	1.0	1.0	1.0	0.001	-0.0007
2022-04-01 00:02:00	1.0	1.0	1.0	0.001	-0.0007
2022-04-01 00:03:00	1.0005	1.0007	1.0	0.001	-0.0007
2022-04-01 00:04:00	1.0005	1.001	1.0001	0.001	-0.0007
2022-04-01 00:05:00	1.0006	1.001	1.0002	-0.0007	-0.0007
2022-04-01 00:06:00	1.0007	1.0011	1.0	-0.0007	-0.0007
2022-04-01 00:07:00	1.0008	1.0012	1.0004	-0.0007	-0.0007
2022-04-01 00:08:00	1.0009	1.0013	1.0005	-0.0007	0.001
2022-04-01 00:09:00	1.001	1.0015	1.0006	-0.0007	0.001
2022-04-01 00:10:00	1.001	1.0015	0.9995	-0.0007	0.001
2022-04-01 00:11:00	1.001	1.0015	0.9993	-0.0007	0.001
2022-04-01 00:12:00	1.001	1.0016	0.999	0.0	0.0

Test and development

This project is based on BDD development. Any change starts here tests/features.

Moreover, you can find the most specific documentation about any module and package of this project.

Tests require behave and pandas. Execute the command below from the source code root directory to install the extra requirements to start developing on this project.

pip install -e ".[dev]"

PYTHONPATH must contain the main source directory ./src to execute the tests. Use the fantastic command as follows.

PYTHONPATH=./src behave tests/features

There are a few integration tests as well. Execute as follows.

PYTHONPATH=./src python tests/integration/test_overcome.py

Author

Jaume Mila Bea [e-mail]