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get_data.py
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get_data.py
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from __future__ import annotations
import datetime
from time import time
from tkinter import E
import json
import pandas as pd
def connect_binance():
"""Connect to binance and return the client"""
import binance
from binance.client import Client
#import credentaials from credentials.json
with open ("credentials.json", "r") as f:
credentials = json.load(f)
api_key = credentials["api_key"]
api_secret = credentials["api_secret"]
client = Client(api_key, api_secret)
return client
def get_klines(client, symbol, interval, start_str, end_str):
"""Get klines from binance and return the data"""
klines = client.get_historical_klines(symbol, interval, start_str, end_str)
return klines
def get_data():
"""Get data from binance and save it to a csv file"""
client = connect_binance()
symbol = "BTCUSDT"
interval = client.KLINE_INTERVAL_4HOUR
start_str = "1 Jan, 2021"
end_str = "1 Feb, 2023"
start = datetime.datetime.strptime(start_str, "%d %b, %Y")
end = datetime.datetime.strptime(end_str, "%d %b, %Y")
#get klines
klines = get_klines(client, symbol, interval, start_str, end_str)
#Create a dataframe
df = pd.DataFrame(klines, columns = ["Open time", "Open", "High", "Low", "Close", "Volume", "Close time", "Quote asset volume", "Number of trades", "Taker buy base asset volume", "Taker buy quote asset volume", "Ignore"])
#Convert the time to datetime
df["Open time"] = pd.to_datetime(df["Open time"], unit = "ms")
df["Close time"] = pd.to_datetime(df["Close time"], unit = "ms")
#Save the data to a csv file
#FORMAT: data/data<timeframe>_<start_date>_<end_date>.csv
df.to_csv("data/data4H_01_01_21_01_02_23.csv", index = False)
#Return the dataframe
print("Data saved to data/data4H_01_01_21_01_02_23.csv")
return df
def get_data_2():
"""Get data from binance and save it to a csv file"""
client = connect_binance()
symbol = "BTCUSDT"
interval = client.KLINE_INTERVAL_12HOUR
start_str = "1 Jan, 2022"
end_str = "1 Feb, 2022"
start = datetime.datetime.strptime(start_str, "%d %b, %Y")
end = datetime.datetime.strptime(end_str, "%d %b, %Y")
#get klines
klines = get_klines(client, symbol, interval, start_str, end_str)
#Create a dataframe
df = pd.DataFrame(klines, columns = ["Open time", "Open", "High", "Low", "Close", "Volume", "Close time", "Quote asset volume", "Number of trades", "Taker buy base asset volume", "Taker buy quote asset volume", "Ignore"])
#Convert the time to datetime
df["Open time"] = pd.to_datetime(df["Open time"], unit = "ms")
df["Close time"] = pd.to_datetime(df["Close time"], unit = "ms")
#Save the data to a csv file
#FORMAT: data/data<timeframe>_<start_date>_<end_date>.csv
df.to_csv("data/data12H_01_01_22_01_02_22.csv", index = False)
#Return the dataframe
return df
def add_simple_indicators(df):
"""Add indicators to the df"""
#MA
for i in range(2, 20):
df[f"MA{i}"] = df["Close"].rolling(window = i).mean()
#EMA
for i in range(2, 20):
df[f"EMA{i}"] = df["Close"].ewm(span = i, adjust = False).mean()
#MACD
df["MACD"] = df["Close"].ewm(span = 12, adjust = False).mean() - df["Close"].ewm(span = 26, adjust = False).mean()
df["Signal"] = df["MACD"].ewm(span = 9, adjust = False).mean()
#Bollinger Bands
df["MA20"] = df["Close"].rolling(window = 20).mean()
df["STD20"] = df["Close"].rolling(window = 20).std()
df["Upper Band"] = df["MA20"] + (df["STD20"] * 2)
df["Lower Band"] = df["MA20"] - (df["STD20"] * 2)
return df
def save_csv(df):
#FORMAT: data/data<timeframe>_<start_date>_<end_date>.csv
df.to_csv("data/data12H_01_01_21_01_02_21.csv", index = False)
def normalise_data(df):
df['Open'] = df['Open'].diff()
df['Open'] = df['Open'] / df['Open'].max()
df['High'] = df['High'].diff()
df['High'] = df['High'] / df['High'].max()
df['Low'] = df['Low'].diff()
df['Low'] = df['Low'] / df['Low'].max()
df['Close'] = df['Close'].diff()
df['Close'] = df['Close'] / df['Close'].max()
df['Volume'] = df['Volume'].diff()
df['Volume'] = df['Volume'] / df['Volume'].max()
df['Quote asset volume'] = df['Quote asset volume'].diff()
df['Quote asset volume'] = df['Quote asset volume'] / df['Quote asset volume'].max()
df['Number of trades'] = df['Number of trades'].diff()
df['Number of trades'] = df['Number of trades'] / df['Number of trades'].max()
df['Taker buy base asset volume'] = df['Taker buy base asset volume'].diff()
df['Taker buy base asset volume'] = df['Taker buy base asset volume'] / df['Taker buy base asset volume'].max()
df['Taker buy quote asset volume'] = df['Taker buy quote asset volume'].diff()
df['Taker buy quote asset volume'] = df['Taker buy quote asset volume'] / df['Taker buy quote asset volume'].max()
for i in range(2, 20):
df[f"MA{i}"] = df[f"MA{i}"].diff()
df[f"MA{i}"] = df[f"MA{i}"] / df[f"MA{i}"].max()
for i in range(2, 20):
df[f"EMA{i}"] = df[f"EMA{i}"].diff()
df[f"EMA{i}"] = df[f"EMA{i}"] / df[f"EMA{i}"].max()
df["MACD"] = df["MACD"].diff()
df["MACD"] = df["MACD"] / df["MACD"].max()
df["Signal"] = df["Signal"].diff()
df["Signal"] = df["Signal"] / df["Signal"].max()
df["MA20"] = df["MA20"].diff()
df["MA20"] = df["MA20"] / df["MA20"].max()
df["STD20"] = df["STD20"].diff()
df["STD20"] = df["STD20"] / df["STD20"].max()
df["Upper Band"] = df["Upper Band"].diff()
df["Upper Band"] = df["Upper Band"] / df["Upper Band"].max()
df["Lower Band"] = df["Lower Band"].diff()
df["Lower Band"] = df["Lower Band"] / df["Lower Band"].max()
df["RSI"] = df["RSI"].diff()
df["RSI"] = df["RSI"] / df["RSI"].max()
df["Stochastic Oscillator"] = df["Stochastic Oscillator"].diff()
df["Stochastic Oscillator"] = df["Stochastic Oscillator"] / df["Stochastic Oscillator"].max()
df["Williams %R"] = df["Williams %R"].diff()
df["Williams %R"] = df["Williams %R"] / df["Williams %R"].max()
df["Upper Band"] = df["Upper Band"].diff()
df["Upper Band"] = df["Upper Band"] / df["Upper Band"].max()
df["Lower Band"] = df["Lower Band"].diff()
df["Lower Band"] = df["Lower Band"] / df["Lower Band"].max()
return df
def change_to_num_simple(df):
df['Open'] = pd.to_numeric(df['Open'])
df['High'] = pd.to_numeric(df['High'])
df['Low'] = pd.to_numeric(df['Low'])
df['Close'] = pd.to_numeric(df['Close'])
df['Volume'] = pd.to_numeric(df['Volume'])
df['Quote asset volume'] = pd.to_numeric(df['Quote asset volume'])
df['Number of trades'] = pd.to_numeric(df['Number of trades'])
df['Taker buy base asset volume'] = pd.to_numeric(df['Taker buy base asset volume'])
df['Taker buy quote asset volume'] = pd.to_numeric(df['Taker buy quote asset volume'])
for i in range(2, 20):
df[f"MA{i}"] = pd.to_numeric(df[f"MA{i}"])
for i in range(2, 20):
df[f"EMA{i}"] = pd.to_numeric(df[f"EMA{i}"])
df["MACD"] = pd.to_numeric(df["MACD"])
df["Signal"] = pd.to_numeric(df["Signal"])
df["MA20"] = pd.to_numeric(df["MA20"])
df["STD20"] = pd.to_numeric(df["STD20"])
df["Upper Band"] = pd.to_numeric(df["Upper Band"])
df["Lower Band"] = pd.to_numeric(df["Lower Band"])
return df
def add_complicated_indicators(df):
#RSI
df["RSI"] = df["Close"].diff().apply(lambda x: x if x > 0 else 0).rolling(window = 14).mean() / df["Close"].diff().apply(lambda x: x if x < 0 else 0).rolling(window = 14).mean()
#Stochastic Oscillator
df["Stochastic Oscillator"] = (df["Close"] - df["Low"].rolling(window = 14).min()) / (df["High"].rolling(window = 14).max() - df["Low"].rolling(window = 14).min())
#Williams %R
df["Williams %R"] = (df["High"].rolling(window = 14).max() - df["Close"]) / (df["High"].rolling(window = 14).max() - df["Low"].rolling(window = 14).min())
#Bollinger Bands
df["STD20"] = df["Close"].rolling(window = 20).std()
df["MA20"] = df["Close"].rolling(window = 20).mean()
df["Upper Band"] = df["MA20"] + (df["STD20"] * 2)
df["Lower Band"] = df["MA20"] - (df["STD20"] * 2)
return df
def new_normalise_df(df):
normalise_df = pd.DataFrame()
for i in range(len(df.columns)):
for j in range(10):
normalise_df[f"{df.columns[i]}_{j}"] = []
normalise_df[f"{df.columns[i]}_{j}_nega"] = []
#Put 0 in all the columns
for i in range(len(df)):
normalise_df.loc[i] = [0 for i in range(len(normalise_df.columns))]
for i in range(len(df.columns)):
for k in range(len(df)):
current = df[f"{df.columns[i]}"][k]
if(current > 0):
if( current < 0.1):
normalise_df[f"{df.columns[i]}_0"][k] = 1
elif(current < 0.2):
normalise_df[f"{df.columns[i]}_1"][k] = 1
elif(current < 0.3):
normalise_df[f"{df.columns[i]}_2"][k] = 1
elif(current < 0.4):
normalise_df[f"{df.columns[i]}_3"][k] = 1
elif(current < 0.5):
normalise_df[f"{df.columns[i]}_4"][k] = 1
elif(current < 0.6):
normalise_df[f"{df.columns[i]}_5"][k] = 1
elif(current < 0.7):
normalise_df[f"{df.columns[i]}_6"][k] = 1
elif(current < 0.8):
normalise_df[f"{df.columns[i]}_7"][k] = 1
elif(current < 0.9):
normalise_df[f"{df.columns[i]}_8"][k] = 1
else:
normalise_df[f"{df.columns[i]}_9"][k] = 1
else:
if(current > -0.1):
normalise_df[f"{df.columns[i]}_0_nega"][k] = 1
elif(current > -0.2):
normalise_df[f"{df.columns[i]}_1_nega"][k] = 1
elif(current > -0.3):
normalise_df[f"{df.columns[i]}_2_nega"][k] = 1
elif(current > -0.4):
normalise_df[f"{df.columns[i]}_3_nega"][k] = 1
elif(current > -0.5):
normalise_df[f"{df.columns[i]}_4_nega"][k] = 1
elif(current > -0.6):
normalise_df[f"{df.columns[i]}_5_nega"][k] = 1
elif(current > -0.7):
normalise_df[f"{df.columns[i]}_6_nega"][k] = 1
elif(current > -0.8):
normalise_df[f"{df.columns[i]}_7_nega"][k] = 1
elif(current > -0.9):
normalise_df[f"{df.columns[i]}_8_nega"][k] = 1
else:
normalise_df[f"{df.columns[i]}_9_nega"][k] = 1
return normalise_df
"""
if __name__ == "__main__":
df = get_data()
df = add_simple_indicators(df)
df = change_to_num_simple(df)
df = add_complicated_indicators(df)
df = normalise_data(df)
save_csv(df)
"""