def average_true_range_percent(close_data, period):
"""
Average True Range Percent.
Formula:
ATRP = (ATR / CLOSE) * 100
"""
catch_errors.check_for_period_error(close_data, period)
atrp = (atr(close_data, period) / np.array(close_data)) * 100
return atrp
def negative_directional_index(close_data, high_data, low_data, period):
"""
Negative Directional Index (-DI).
Formula:
-DI = 100 * SMMA(-DM) / ATR
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data)
ndi = (100 *
smma(negative_directional_movement(high_data, low_data), period) /
atr(close_data, period))
return ndi
def positive_directional_index(close_data, high_data, low_data, period):
"""
Positive Directional Index (+DI).
Formula:
+DI = 100 * SMMA(+DM) / ATR
"""
catch_errors.check_for_input_len_diff(close_data, high_data, low_data)
pdi = (100 *
smma(positive_directional_movement(high_data, low_data), period) /
atr(close_data, period))
return pdi
def screen():
client = Client("api-key", "api-secret", {"verify": False, "timeout": 20})
positiveCoin = get_positive_coin.positiveCoin
bullishKScoin = []
for m in positiveCoin:
candles = client.get_klines(symbol=m,
interval=client.KLINE_INTERVAL_30MINUTE)
if candles[-2][1] < candles[-2][4] <= candles[-1][1] < candles[-1][4]:
bullishKScoin.append(m)
atrCoin = []
for m in bullishKScoin:
candles = client.get_klines(symbol=m,
interval=client.KLINE_INTERVAL_5MINUTE)
close = []
for n in candles:
close.append(float(n[4]))
avgtr = atr(close, 14)
if avgtr[-1] / close[-1] > 0.01:
atrCoin.append(m)
maxDemandRatio = 0
buyingCoin = ''
for m in atrCoin:
depth = client.get_order_book(symbol=m)
buyingVol = 0
sellingVol = 0
for n in depth['bids'][0:20]:
buyingVol = buyingVol + float(n[1])
for n in depth['asks'][0:20]:
sellingVol = sellingVol + float(n[1])
demandRatio = buyingVol / sellingVol
print(demandRatio)
print(maxDemandRatio)
if demandRatio > maxDemandRatio:
maxDemandRatio = demandRatio
buyingCoin = m
if maxDemandRatio < 1.5:
buyingCoin = ''
print(bullishKScoin)
print(atrCoin)
print(buyingCoin)
return buyingCoin
def get_analysis(self, data):
return atr(data, self.params['period'])[-1]
def average_true_range(data, period):
return atr(data, period)