Grafico MACD e prezzo

import requests

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

from datetime import datetime

def get_binance_data(symbol, interval, limit):

    url = f"https://api.binance.com/api/v3/klines?symbol={symbol}&interval={interval}&limit={limit}"

    response = requests.get(url)

    data = response.json()

    df = pd.DataFrame(data)

    df.columns = ['timestamp', 'open', 'high', 'low', 'close', 'volume', 'close_time', 'quote_asset_volume', 'number_of_trades', 'taker_buy_base_asset_volume', 'taker_buy_quote_asset_volume', 'ignore']

    df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')

    df['close'] = df['close'].astype(float)

    return df

def calculate_moving_average(data, window):

    return data['close'].rolling(window=window).mean()

def calculate_macd(data, fast_period, slow_period, signal_period):

    macd = data['close'].ewm(span=fast_period, adjust=False).mean() - data['close'].ewm(span=slow_period, adjust=False).mean()

    signal = macd.ewm(span=signal_period, adjust=False).mean()

    histogram = macd - signal

    return macd, signal, histogram

def plot_chart(data, ma_50, ma_99, macd, signal, histogram):

    fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True, figsize=(12, 8))

    ax1.plot(data['timestamp'], data['close'], label='Close')

    ax1.plot(data['timestamp'], ma_50, label='MA 50')

    ax1.plot(data['timestamp'], ma_99, label='MA 99')

    ax1.legend()

    ax2.plot(data['timestamp'], macd, label='MACD')

    ax2.plot(data['timestamp'], signal, label='Signal')

    ax2.bar(data['timestamp'], histogram, label='Histogram', color='gray')

    ax2.legend()

    plt.xlabel('Timestamp')

    plt.show()

symbol = 'BTCUSDT'

interval = '1d'

limit = 500

data = get_binance_data(symbol, interval, limit)

ma_50 = calculate_moving_average(data, 50)

ma_99 = calculate_moving_average(data, 99)

macd, signal, histogram = calculate_macd(data, 12, 26, 9)

plot_chart(data, ma_50, ma_99, macd, signal, histogram)

PYTHON - MA and BOOKORDER SCALPING

Scalping signal indicator written in python. It is based on Binance data.
The simpleBinanceFut.py module can be downloaded from GitHub.
https://github.com/DanielePorcari/simpleBinanceFutures

import simpleBinanceFut as sBF

def MA(dati,periodo):
    ris=0
    i=len(dati)-1
    for c in range(periodo):
        if i>=periodo:
            prezzo=(float(dati[i][4]))
            ris=ris+prezzo
            i-=1
    ris=ris/periodo
    return ris

def bookBot(up):
    volMax=0
    priceMax=0
    if up:
        for i in range(len(book['bids'])):
            price=float(book['bids'][i][0])
            vol=float(book['bids'][i][1])
            if float(vol)>float(volMax):
                volMax=vol
                priceMax=price
        print("Buy: ",priceMax)
        volMax=0
        priceMax=0
        for i in range(len(book['asks'])):
            price=book['asks'][i][0]
            vol=book['asks'][i][1]
            if float(vol)>float(volMax):
                volMax=vol
                priceMax=price
        print("TP: ",priceMax)
    else:
        for i in range(len(book['asks'])):
            price=float(book['asks'][i][0])
            vol=float(book['asks'][i][1])
            if float(vol)>float(volMax):
                volMax=vol
                priceMax=price
        print("Sell: ",priceMax)
        volMax=0
        priceMax=0
        for i in range(len(book['bids'])):
            price=book['bids'][i][0]
            vol=book['bids'][i][1]
            if float(vol)>float(volMax):
                volMax=vol
                priceMax=price
        print("TP: ",priceMax)

coppia=input("Insert asset: ")
print("Last price: ",sBF.fbin_ultimo_prezzo(coppia))
tf=input("Insert tf:")
book=sBF.fbin_book(coppia)
prezzi=sBF.fbin_candlestick(coppia,tf,"99")
ma9=MA(prezzi,50)
ma25=MA(prezzi,25)
if ma9>ma25:
    up=True
else:
    up=False
bookBot(up)

PYTHON BINANCE API - HOW TO MAKE: A LIST OF SMA and MACD

This little python script uses the Binance API.
The customer position is opened to access the data.
Data on the chosen pair are retrieved in the chosen time frame.
The moving average vector is constructed.
The macd vector is constructed.
The macd vector is drawn.
As always if you like the blog you can help me by going on by donating BTC, Link, BAT or by donating through gofundme.

from binance.client import Client
from twisted.internet import reactor
from binance.enums import *
import matplotlib.pyplot as plt
import dateparser

def recuperoDati(coppia,tf):
    f=open('chiavi.dat')
    #esempio di chiavi.dat file: chiavepubblicabinance-chiaveprivatabinance
    #tra le due chiavi deve esserci '-' senza spazi, alla fine della riga non
    #deve essere battuto invio
    rawkeys=f.read()
    f.close()
    k=rawkeys.split('-')
    client=Client(api_key=k[0], api_secret=k[1])
    if tf=='DAY':
        rawData=client.get_klines(symbol=coppia, interval=client.KLINE_INTERVAL_1DAY)
    elif tf=='4H':
        rawData=client.get_klines(symbol=coppia, interval=client.KLINE_INTERVAL_4HOUR)
    elif tf=='30M':
        rawData=client.get_klines(symbol=coppia, interval=client.KLINE_INTERVAL_30MINUTE)
    elif tf=='15M':
        rawData=client.get_klines(symbol=coppia, interval=client.KLINE_INTERVAL_15MINUTE)
    elif tf=='5M':
        rawData=client.get_klines(symbol=coppia, interval=client.KLINE_INTERVAL_5MINUTE)
    return rawData

def vettoreMA(dati,periodo):
    itad=[]
    vMediaINV=[]
    mediaFinale=[]
    i=len(dati)
    c=0
    prov=0
    conta=0
    while i >0 :
        itad.append(dati[c])
        c+=1
        i-=1
    long1=len(itad)//int(periodo)
    for i in range(len(itad)-int(periodo)):
        for c in range(periodo):
            prov=float(itad[i+c][4])+prov
        vMediaINV.append(prov/periodo)
        prov=0
    i=len(vMediaINV)
    c=0
    while i >0:
        mediaFinale.append(vMediaINV[c])
        c+=1
        i-=1
    return mediaFinale

def vettoreMACD(mediasmall,mediabig):
    macd=[]
    for i in range(len(mediabig)):
        macd.append(mediabig[i]-mediasmall[i])
    return macd

def asseX(l,dati):
    x=[]
    for i in range(l):
        data=dateparser.parse(str(dati[i][0]))
        data=str(data)
        oD=data.split(" ")
        x.append(oD[0])
    return x

rawData=recuperoDati('BATUSDT','DAY')
ms=vettoreMA(rawData,12)
mb=vettoreMA(rawData,26)
macd=vettoreMACD(ms,mb)
x=asseX(len(macd),rawData)
fig=plt.figure(figsize=(20,17))
plt.plot(x, macd, color = 'black')
plt.show()

PYTHON API BINANCE - IA - PIVOT POINT AUTOMATIC -

BOT trading for crypto currencies based on Binance API. The pivot point is automatically calculated on the last important candle of the selected time frame, over the chosen days. The candle is selected based on its amplitude and volume. The results are printed on the monitor and on a graph.

BOT di trading per crypto valute basato su Binance API. Il pivot point viene calcolato in automatico sull'ultima candela importante del time frame selezionato, nell'arco dei giorni scelti. La candela viene selezionata in base all'ampiezza e il volume. I risultati sono stampati a monitor e su grafico.

You can download the zip of the executable file on windows from this link
Puoi scaricare lo zip del file eseguibile su windows da questo link:  Pivot Calc