Calculate Support and Resistance

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Tags: #ccxt #bitcoin #trading
Author: Jeremy Ravenel​
Input
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!pip install trendln matplotlib==3.1.3 --user
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import naas
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import ccxt
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import pandas as pd
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from datetime import datetime
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import naas_drivers
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import trendln
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import plotly.tools as tls
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import plotly.graph_objects as go
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Setup Binance
👉 How to create API ?​
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binance_api = ""
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binance_secret = ""
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Variables
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symbol = 'BTC/USDT'
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limit = 180
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timeframe = '4h'
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Model
Get data
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binance = ccxt.binance({
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    'apiKey': binance_api,
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    'secret': binance_secret
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}) 
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​
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data = binance.fetch_ohlcv(symbol=symbol,
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                           limit=limit,
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                           timeframe=timeframe)
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Data cleaning
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df = pd.DataFrame(data, columns=["Date", "Open", "High", "Low", "Close", "Volume"])
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df['Date'] = [datetime.fromtimestamp(float(time)/1000) for time in df['Date']]
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df
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Output
Plotting figure
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fig = trendln.plot_support_resistance(
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    df[-1000:].Close, #as per h for calc_support_resistance
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    xformatter = None, #x-axis data formatter turning numeric indexes to display output
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      # e.g. ticker.FuncFormatter(func) otherwise just display numeric indexes
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    numbest = 1, #number of best support and best resistance lines to display
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    fromwindows = True, #draw numbest best from each window, otherwise draw numbest across whole range
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    pctbound = 0.1, # bound trend line based on this maximum percentage of the data range above the high or below the low
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    extmethod = trendln.METHOD_NUMDIFF,
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    method=trendln.METHOD_PROBHOUGH,
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    window=125,
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    errpct = 0.005,
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    hough_prob_iter=50,
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    sortError=False,
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    accuracy=1)
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plotly_fig = tls.mpl_to_plotly(fig)
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layout = dict(
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    dragmode="pan",
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    xaxis_rangeslider_visible=False,
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    showlegend=True,
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)
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new_data = list(plotly_fig.data)
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new_data.pop(2)
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new_data.pop(2)
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new_data.pop(1)
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new_data.pop(1)
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fig = go.Figure(data=new_data, layout=layout)
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fig
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CCXT

Predict Bitcoin from Binance

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Contents

Input

Model

Output