9.07 Conditionning model

We reproduce here the figure 9.8 of the book. Utilitary functions can be found next to this file. Here, we only define codpy-related functions.

Necessary Imports

import os
import sys

import matplotlib.pyplot as plt
import numpy as np

from codpy.kernel import Sampler

try:
    CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
except NameError:
    CURRENT_DIR = os.getcwd()
data_path = os.path.join(CURRENT_DIR, "data")
PARENT_DIR = os.path.abspath(os.path.join(CURRENT_DIR, ".."))
sys.path.insert(0, PARENT_DIR)

import utils.ch9.mapping as maps
from utils.ch9.data_utils import stats_df
from utils.ch9.market_data import retrieve_market_data
from utils.ch9.path_generation import generate_paths
from utils.ch9.plot_utils import display_historical_vs_generated_distribution

Parameter definition

def get_cdpres_param():
    return {
        "rescale_kernel": {"max": 2000, "seed": None},
        "rescale": True,
        "grid_projection": True,
        "reproductibility": False,
        "date_format": "%d/%m/%Y",
        "begin_date": "01/06/2020",
        "end_date": "01/06/2022",
        "today_date": "01/06/2022",
        "symbols": ["AAPL", "GOOGL", "AMZN"],
    }

Get the market data

params = retrieve_market_data()

Defining the map

The conditionner model defines the noise to be conditioned by the process itself:

\[\ln X^{k+1} = \ln X^k + \varepsilon^k \mid \ln X^k,\]

params["map"] = maps.composition_map(
    (maps.QuantileConditioner_map(params), maps.log_map, maps.remove_time())
)
params = maps.apply_map(params)

We define our sampler on the mapped data using codpy’s Sampler

You can define your own latent generator function, here we use a simple uniform distribution. But if not provided, a default one will be used by the Sampler class.

mapped_data = params["transform_h"].values
generator = lambda n: np.random.uniform(size=(n, mapped_data.shape[1]))
sampler = Sampler(mapped_data, latent_generator=generator)
params["sampler"] = sampler

We plot the original distribution vs the generated one

params = display_historical_vs_generated_distribution(params)
params["graphic"](params)
plt.show()

Reproductibility test

We regenerate the same path by generating from the latent representation We make sure we get the original data back.

params["reproductibility"] = True
params = generate_paths(params)
params["graphic"](params)
plt.show()

We now generate a new set of 10 paths

params["reproductibility"] = False
params["Nz"] = 10
params = generate_paths(params)
params["graphic"](params)
plt.show()

stats = stats_df(params["transform_h"], params["transform_g"]).T
print(stats)

                        0               1                2
Mean      -0.0011(0.0066)  0.0022(-0.043)  -0.0022(-0.015)
Variance     -0.011(0.05)     0.003(0.15)     0.001(0.089)
Skewness        0.5(0.51)       0.5(0.44)         0.5(0.5)
Kurtosis    -0.033(-0.48)    -0.09(-0.34)    -0.023(-0.51)
KS test        0.97(0.05)      0.14(0.05)       0.66(0.05)