carloocchiena

# copy and paste the script on a Jupyter Notebook to have the 
# table printed on screen

import numpy as np
import pandas as pd
from scipy.integrate import quad

def normal_probability_density(x):
    constant = 1.0 / np.sqrt(2 * np.pi)
    return(constant * np.exp((-x**2) / 2))

carloocchiena

app.location.recordings.each(function (recording) { 
  recording.tracks.each(function (track) { 
    track.downloadFromLocal(window.location.href); 
  }) 
})

carloocchiena

Cargo CLI commands

Source: https://doc.rust-lang.org/cargo/commands/general-commands.html

bold = very useful

cargo --version (actual version)
cargon --list (list installed commands)
cargo --help (provide helps)
cargo --explain code (explain specific functions)

carloocchiena

-- create our table
CREATE TABLE Videogiochi (
    ID INT PRIMARY KEY AUTO_INCREMENT,
    Nome VARCHAR(100) NOT NULL,
    AnnoUscita INT NOT NULL,
    Rating INT CHECK (Rating >= 1 AND Rating <= 5)
);

-- insert the data
INSERT INTO Videogiochi (Nome, AnnoUscita, Rating) VALUES

carloocchiena

"""
I'm starting to like the Variance-Gamma model quite a bit.
Here is a quick and dirty Octave (Matlab) code for generating the IV skew given VG parameters
I took the parameters from the Madan, Carr and Chang paper.
Instead of evaluating a double integral I use the mixing formula, which results in a single integral.
From Linkedin profile of Frido Rolloos
""


import numpy as np

carloocchiena

# --- 1. Libraries ---
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import timedelta

# --- 2. Load the Excel file ---
# Replace with your actual filename
df = pd.read_excel('20240101_20241231_sample.xlsx')

carloocchiena

# Add useful time-based features
df['Hour'] = df.index.hour
df['Day'] = df.index.day
df['Weekday'] = df.index.weekday  # Monday=0, Sunday=6
df['Month'] = df.index.month
df['Date'] = df.index.date  # useful for grouping by day

plt.figure(figsize=(10, 4))
sns.histplot(df['Value'], bins=50, kde=True)
plt.title('Distribution of values')

carloocchiena

import matplotlib.dates as mdates

# 1. Riorganizza i dati: pivot con giorni sulle righe, ore sulle colonne
heatmap_data = df.copy()
heatmap_data['Date'] = heatmap_data.index.date
heatmap_data['Hour'] = heatmap_data.index.hour

pivot = heatmap_data.pivot_table(index='Date', columns='Hour', values='Value')

# 2. Plot della heatmap

carloocchiena

from statsmodels.tsa.statespace.sarimax import SARIMAX
from sklearn.metrics import mean_squared_error

# Drop NaNs from differencing (if any)
daily_data = df['Value'].resample('D').mean()
daily_data = daily_data.asfreq('D') # daily average
daily_data = daily_data.fillna(method='ffill')  # safety fill if any missing

subset = daily_data['2024-01-01':'2024-03-31']