RevoStaffone

## pckr

packer {
  required_plugins {
    amazon = {
      version = ">= 1.1.3"
      source  = "github.com/hashicorp/amazon"
    }
  }
}

## db-sport
SET FOREIGN_KEY_CHECKS=0;
SET SQL_MODE = "NO_AUTO_VALUE_ON_ZERO";
START TRANSACTION;
SET time_zone = "+00:00";

DROP TABLE IF EXISTS `invitation`;
CREATE TABLE `invitation` (
  `id_invitation` int(11) NOT NULL,
  `type` enum('team','tournoi') NOT NULL,
  `id_team` int(11) DEFAULT NULL,

## gist:34d34c44d2b88756aa7e3f400cfb719f
// ==UserScript==
// @name        Mafia 13N
// @version     2024.11.17
// @match       https://www.blsspainmorocco.net/*
// @match       https://mail.google.com/*
// @run-at      document-end
// @inject-into page
// @noframes
// @grant       unsafeWindow
// @grant       GM.setValue

## gist:bf19fb694744cfe0403857c9d49a97f4
import React, { useState, useEffect, useRef } from 'react';
import {
  View,
  TextInput,
  StyleSheet,
  Text,
  ActivityIndicator,
  Animated,
  Easing,
  Platform,

## api_signup.php
<?php
header("Content-Type: application/json; charset=UTF-8");
header("Access-Control-Allow-Origin: *");
header("Access-Control-Allow-Headers: Origin, X-Requested-With, Content-Type, Accept");
error_reporting(0);
require('../functions.php');         // ON DÉFINI LES FUNCTIONS
include('../config.php');            // ON SE CONNECTE A LA BASE DE DONNÉES
include('../config_PDO.php');
require('../theCallR_2/envoi_sms_2.php');
include('../002_packages/jwt/JWT.php');

## backtesting.py
import pandas as pd
from keras.models import load_model
import numpy as np
import matplotlib.pyplot as plt

symbol = 'CFX'

# Step 1: Load historical price data and extracted features
historical_data = pd.read_csv(symbol+'/data_'+symbol+'.csv')
features_data = pd.read_csv(symbol+'/'+symbol+'_features.csv')

## features_extraction.py
import pandas as pd
import numpy as np

def add_technical_indicators(df):
    # Calculate 7-day and 30-day Moving Averages
    df['MA_7'] = df['close'].rolling(window=7).mean()
    df['MA_30'] = df['close'].rolling(window=30).mean()

    # Calculate Relative Strength Index (RSI)
    delta = df['close'].diff()

## get_data.py
import requests
import pandas as pd

def get_historical_data(symbol, currency, limit, toTs=None):
    base_url = 'https://min-api.cryptocompare.com/data/v2/histominute'
    params = {
        'fsym': symbol,
        'tsym': currency,
        'limit': limit,
    }

## model_selection.py
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
from keras.models import Sequential
from keras.layers import LSTM, Dense


symbol = 'cfx'
# Load the data from the CSV file into a DataFrame

## gist:5b583160e187e14ac2ef36e79abe104d
<?php  ///////////////////////////////////////////////////////////////////////////////////////////////////////////
		     include("scripts/bandeau_style.php");
       ////////////////////////////////////////////////////////////////////////////////////////////////////////////
?>
</head>
<!-- END HEAD -->
<!-- BEGIN BODY -->
<body class="page-header-fixed"> <!-- Sidebar Closed Page-->
<!-- BEGIN HEADER -->
	     <?php  ///////////////////////////////////////////////////////////////////////////////////////////////////////////

	packer {
	required_plugins {
	amazon = {
	version = ">= 1.1.3"
	source = "github.com/hashicorp/amazon"
	}
	}
	}
	SET FOREIGN_KEY_CHECKS=0;
	SET SQL_MODE = "NO_AUTO_VALUE_ON_ZERO";
	START TRANSACTION;
	SET time_zone = "+00:00";

	DROP TABLE IF EXISTS `invitation`;
	CREATE TABLE `invitation` (
	`id_invitation` int(11) NOT NULL,
	`type` enum('team','tournoi') NOT NULL,
	`id_team` int(11) DEFAULT NULL,
	// ==UserScript==
	// @name Mafia 13N
	// @version 2024.11.17
	// @match https://www.blsspainmorocco.net/*
	// @match https://mail.google.com/*
	// @run-at document-end
	// @inject-into page
	// @noframes
	// @grant unsafeWindow
	// @grant GM.setValue
	import React, { useState, useEffect, useRef } from 'react';
	import {
	View,
	TextInput,
	StyleSheet,
	Text,
	ActivityIndicator,
	Animated,
	Easing,
	Platform,
	<?php
	header("Content-Type: application/json; charset=UTF-8");
	header("Access-Control-Allow-Origin: *");
	header("Access-Control-Allow-Headers: Origin, X-Requested-With, Content-Type, Accept");
	error_reporting(0);
	require('../functions.php'); // ON DÉFINI LES FUNCTIONS
	include('../config.php'); // ON SE CONNECTE A LA BASE DE DONNÉES
	include('../config_PDO.php');
	require('../theCallR_2/envoi_sms_2.php');
	include('../002_packages/jwt/JWT.php');
	import pandas as pd
	from keras.models import load_model
	import numpy as np
	import matplotlib.pyplot as plt

	symbol = 'CFX'

	# Step 1: Load historical price data and extracted features
	historical_data = pd.read_csv(symbol+'/data_'+symbol+'.csv')
	features_data = pd.read_csv(symbol+'/'+symbol+'_features.csv')
	import pandas as pd
	import numpy as np

	def add_technical_indicators(df):
	# Calculate 7-day and 30-day Moving Averages
	df['MA_7'] = df['close'].rolling(window=7).mean()
	df['MA_30'] = df['close'].rolling(window=30).mean()

	# Calculate Relative Strength Index (RSI)
	delta = df['close'].diff()
	import requests
	import pandas as pd

	def get_historical_data(symbol, currency, limit, toTs=None):
	base_url = 'https://min-api.cryptocompare.com/data/v2/histominute'
	params = {
	'fsym': symbol,
	'tsym': currency,
	'limit': limit,
	}
	<?php ///////////////////////////////////////////////////////////////////////////////////////////////////////////
	include("scripts/bandeau_style.php");
	////////////////////////////////////////////////////////////////////////////////////////////////////////////
	?>
	</head>
	<!-- END HEAD -->
	<!-- BEGIN BODY -->
	<body class="page-header-fixed"> <!-- Sidebar Closed Page-->
	<!-- BEGIN HEADER -->
	<?php ///////////////////////////////////////////////////////////////////////////////////////////////////////////