rohithteja

# download daily crypto prices from Yahoo Finance
df = yf.download(tickers=f'{select_token}-{select_fiat}', period = '1d', interval = '1m')

# Plotly line chart
fig = go.Figure()
fig.add_scattergl(x=df.index, y=df.Close, 
                  line={'color': 'green'},name='Up trend')
fig.add_scattergl(x=df.index, y=df.Close.where(df.Close <= df.Open[0]), 
                  line={'color': 'red'},name='Down trend')
fig.add_hline(y=df.Open[0])

rohithteja

import yfinance as yf
import pandas as pd
import plotly.graph_objs as go

# download 5 year crypto prices from Yahoo Finance
df = yf.download(tickers='BTC-USD', period = '5y', interval = '1d')

# compute moving averages
df['MA100'] = df.Close.rolling(100).mean()
df['MA50'] = df.Close.rolling(50).mean()

rohithteja

#-------------------
# Web scraping Yahoo Finance
#-------------------
from bs4 import BeautifulSoup
import requests
import pandas as pd

dic = {}
url = 'https://finance.yahoo.com/cryptocurrencies?offset=0&count=100'
soup = BeautifulSoup(requests.get(url).text)

rohithteja

import torch
import pandas as pd
import networkx as nx
import torch.nn as nn
import torch.nn.functional as F
import torch_geometric.transforms as T
from torch_geometric.nn import ChebConv
from torch_geometric.data import InMemoryDataset, Data
from sklearn.model_selection import train_test_split

rohithteja

import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn import metrics

# read data
classes = pd.read_csv("../input/elliptic-data-set/elliptic_bitcoin_dataset/elliptic_txs_classes.csv")
edgelist = pd.read_csv("../input/elliptic-data-set/elliptic_bitcoin_dataset/elliptic_txs_edgelist.csv")
features = pd.read_csv("../input/elliptic-data-set/elliptic_bitcoin_dataset/elliptic_txs_features.csv", header=None)

rohithteja

import plotly.express as px
import imageio

# removing small countries and NA values
geo = shapefile.dropna()
geo = geo.reset_index(drop=True)
geo = geo[['iso3','name','geometry']]

# appending temperature values to geo DF
for i in range(df_temp.shape[0]):

rohithteja

import datetime
import geopandas as gpd
import pandas as pd
import numpy as np
from shapely.geometry import mapping
import rioxarray

# read shapefile
shapefile = gpd.read_file("data/world-administrative-boundaries/world-administrative-boundaries.shp")

rohithteja

import xarray as xr

ds = xr.open_dataset('2mtemp_2021.nc')
ds = ds.resample(time='M').mean() #resample monthly

#bounds
min_lon = -128 
min_lat = 19
max_lon = -67
max_lat = 50

rohithteja

embedding_dim = 100
optimizer = best_params['optimizer']

model = Sequential()
model.add(layers.Embedding(input_dim=vocab_size, 
                        output_dim=embedding_dim,
                        input_length=maxlen))
model.add(SpatialDropout1D(0.4))
model.add(LSTM(64, activation="tanh"))
model.add(Dense(3,activation='softmax'))

rohithteja

import optuna
from optuna.trial import TrialState
from sklearn.metrics import accuracy_score

def objective(trial):
  optimizer_name = trial.suggest_categorical("optimizer", ["adam", "SGD", "RMSprop", "Adadelta"])
  epochs = trial.suggest_int("epochs", 5, 15,step=5, log=False)
  batchsize = trial.suggest_int("batchsize", 8, 40,step=16, log=False)

  history, model = lstm(optimizer_name,epochs,batchsize)