alvarobartt

# Python3.x
import investpy
import pandas as pd

import random

from pprint import pprint


# This function retrieves all the available equities indexed on es.Investing.com

alvarobartt

#!/usr/bin/env python

# Copyright 2018-2019 Alvaro Bartolome
# See LICENSE for details.

__author__ = "Alvaro Bartolome <[email protected]>"

import investpy
import pandas as pd
import datetime

alvarobartt

import trendet
    
import matplotlib.pyplot as plt
import seaborn as sns

sns.set(style='darkgrid')

df = identify_all_trends(equity='bbva',
                         country='spain',
                         from_date='01/01/2018',

alvarobartt

import investpy

print(investpy.__author__, investpy.__version__)

# Retrieve a `pandas.DataFrame` containing all the Stocks from United States
stocks = investpy.get_stocks(country='united states') 

# Retrieve historical data from AAPL
df = investpy.get_stock_historical_data(stock='AAPL', # Stock symbol of the stock to retrieve data from
                                        country='united states', # Country from where the introduced stock is from

alvarobartt

# Required investpy version 0.9.10 or higher (0.9.12 recommended)
import investpy

# As asked on issue #86 -> https://github.com/alvarob96/investpy/issues/86
# this gist will explain `investpy.search_text` function usage for Swedish
# bonds that are not statically stored on investpy/resources/bonds/bonds.csv
# file, since its data is not provided by Investing on its general government
# bonds listing.
search_results = investpy.search_text(text='Sweden 10 Year', count=10)

alvarobartt

# Copyright 2020 Alvaro Bartolome @ alvarobartt in GitHub

#   ____  ___ ___  ______      ______  ____  ___ ___    ___  _____   ___   ____     ___ 
#  /    ||   |   ||      |    |      ||    ||   |   |  /  _]|     | /   \ |    \   /  _]
# |   __|| _   _ ||      |    |      | |  | | _   _ | /  [_ |__/  ||     ||  _  | /  [_ 
# |  |  ||  \_/  ||_|  |_|    |_|  |_| |  | |  \_/  ||    _]|   __||  O  ||  |  ||    _]
# |  |_ ||   |   |  |  |        |  |   |  | |   |   ||   [_ |  /  ||     ||  |  ||   [_ 
# |     ||   |   |  |  |        |  |   |  | |   |   ||     ||     ||     ||  |  ||     |
# |___,_||___|___|  |__|        |__|  |____||___|___||_____||_____| \___/ |__|__||_____|
                                                                                      

alvarobartt

import torch
from datasets import load_dataset
from peft import LoraConfig, get_peft_model
from transformers import AutoTokenizer, AutoModelForCausalLM
from trl import DPOTrainer


if __name__ == "__main__":
	model_name = "..."
	dataset = load_dataset(...)

alvarobartt

# Usage: python upload.py --dir <dir> --hub-name <hub_name>

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

import argparse


def get_args():
    parser = argparse.ArgumentParser()

alvarobartt

# Usage:
# python steps-unpacked-constant-length-dataset.py \
# 	--dataset-path "argilla/ultrafeedback-binarized-preferences-cleaned" \
#	--hf-tokenizer "alignment-handbook/zephyr-7b-sft-full" \
# 	--gradient-accumulation-steps 2 \
# 	--per-eval-batch-size 32 \
# 	--num-devices 8 \
# 	--max-seq-length 2048 \
# 	--num-of-sequences 1024 \
# 	--chars-per-token 3.6

alvarobartt

from distilabel.llms import (
    AnthropicLLM,
    InferenceEndpointsLLM,
    OpenAILLM,
)
from distilabel.pipeline import Pipeline
from distilabel.steps import (
    CombineColumns,
    KeepColumns,
    LoadDataFromDicts,