async def support_resistance(market, candles):
close_prices = [float(i['mid']['c']) for i in candles]
data = np.array(close_prices)
close_series = pd.Series(data)
(minimaIdxs, pmin, mintrend,
minwindows), (maximaIdxs, pmax, maxtrend,
maxwindows) = trendln.calc_support_resistance(close_series)
fig = trendln.plot_support_resistance(close_series)
# plt.savefig('suppres_{}_{}.svg'.format(market, str(datetime.now())), format='svg')
# plt.show()
plt.clf()
return minimaIdxs, pmin, mintrend, minwindows, maximaIdxs, pmax, maxtrend, maxwindows
def drawChart(hist, update):
mins, maxs = trendln.calc_support_resistance(hist[-1000:].close)
minimaIdxs, pmin, mintrend, minwindows = trendln.calc_support_resistance(
(hist[-1000:].low, None)) #support only
mins, maxs = trendln.calc_support_resistance(
(hist[-1000:].low, hist[-1000:].high))
(minimaIdxs, pmin, mintrend, minwindows), (maximaIdxs, pmax, maxtrend,
maxwindows) = mins, maxs
minimaIdxs, maximaIdxs = trendln.get_extrema(hist[-1000:].close)
maximaIdxs = trendln.get_extrema((None, hist[-1000:].high)) #maxima only
minimaIdxs, maximaIdxs = trendln.get_extrema(
(hist[-1000:].low, hist[-1000:].high))
fig = trendln.plot_support_resistance(
hist[-1000:].close) # requires matplotlib - pip install matplotlib
#if not update:
# plt.savefig('suppres.svg', format='svg')
# plt.show()
# plt.clf() #clear figure
fig = trendln.plot_sup_res_date((hist[-1000:].low, hist[-1000:].high),
hist[-1000:].index) #requires pandas
if not update:
plt.show()
curdir = '.'
end = '2020-05-4' #timeframe = '3mo' #30d #1mo, 3mo, max, 1y
hist = yf.download(
t, start, end) # tick.history(period=timeframe, rounding=True)
mins, maxs = trendln.calc_support_resistance(hist[-1000:].Close)
minimaIdxs, pmin, mintrend, minwindows = trendln.calc_support_resistance(
(hist[-1000:].Low, None)) #support only
mins, maxs = trendln.calc_support_resistance(
(hist[-1000:].Low, hist[-1000:].High))
(minimaIdxs, pmin, mintrend, minwindows), (maximaIdxs, pmax, maxtrend,
maxwindows) = mins, maxs
minimaIdxs, maximaIdxs = trendln.get_extrema(hist[-1000:].Close)
maximaIdxs = trendln.get_extrema(
(None, hist[-1000:].High)) #maxima only
minimaIdxs, maximaIdxs = trendln.get_extrema(
(hist[-1000:].Low, hist[-1000:].High))
fig = trendln.plot_support_resistance(
hist[-1000:].Close) # requires matplotlib - pip install matplotlib
t1 = t
t = t + "_" + start + "_" + end + ".png"
file = os.path.join(filepath, t)
t1 = t1 + "_" + start + "_" + end + "_" + ".png"
print("\n Volume ratio: latest day/previous day:",
round(hist['Volume'][-1] / hist['Volume'][-2], 4), ",",
round(hist['Volume'][-2] / hist['Volume'][-3], 4), ",",
round(hist['Volume'][-3] / hist['Volume'][-4], 4), ",",
round(hist['Volume'][-4] / hist['Volume'][-5], 4), ",",
round(hist['Volume'][-5] / hist['Volume'][-6], 4))
file1 = os.path.join(filepath, t1)
plt.savefig(file1, format='png')
print(t, t1)
fig = trendln.plot_sup_res_date((hist[-1000:].Low, hist[-1000:].High),
hist[-1000:].index)
import trendln
import yfinance as yf
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import datetime
from pandas_datareader import data as pdr
yf.pdr_override()
stock = 'AAPL'
num_of_years = 1
start_date = datetime.datetime.now() - datetime.timedelta(
int(365.25 * num_of_years))
end_date = datetime.datetime.now()
delta = start_date - end_date
x_data = np.arange(delta)
hist = pdr.get_data_yahoo(stock, start_date, end_date)
print(hist)
fig = trendln.plot_support_resistance(hist[-1000:].Close)
plt.show()