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neonEvaluator.py
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import json
import socket
import os
import cv2 as cv
import random
import time
# from talib import ATR as talibATR
import numpy as np
import csv
import statistics
import sys
import math
# from scipy.signal import find_peaks
# ====================================================>
# =========== GLOBAL SETTINGS
# ====================================================>
# TODO move to configfile ones which possible to move
# ====================================================>
TOKEN_NAME = "UNKNOWN"
TOKEN_INDEX = 0
TEST_CASE = "UNKNOWN"
VALIDATION_MODE = False
MA_LAG = 0
LOG_TOLERANCE = 3
FETCHER_PORT = 7777
INTERFACE_PORT = 6666
# VISUALISE = False
VISUALISE = True
LOGGING = True
# LOGGING = False
timeframe = 30
# timeframe = 1
# timeframe = 4
RANDOM = None
SIGNALS_LOG = []
RECORD_STATS = True
# isFirst = True
isFirst = False
INTERVAL_M = 30
# INTERVAL_M = 1
INTERVAL = f"{INTERVAL_M}m"
if INTERVAL_M == 15:
TIMEFRAME = "3d"
elif INTERVAL_M == 10:
TIMEFRAME = "2d"
elif INTERVAL_M == 30:
TIMEFRAME = "30m"
else:
TIMEFRAME = "1d"
INTERVAL_M = INTERVAL_M / 2
MODEL = None
# ====================================================>
# =========== NOTATION
# ====================================================>
N_UNDEFINED = 1 << 0
N_BEARISH = 1 << 1
N_BULLISH = 1 << 2
N_BAD = 1 << 3
N_LONG = 1 << 4
N_SHORT = 1 << 5
N_BULL_SS = 1 << 6
N_BEAR_SS = 1 << 7
N_BULL_DT = 1 << 8
N_BEAR_DT = 1 << 9
N_BULL_ACC = 1 << 10
N_BEAR_ACC = 1 << 11
N_TRAP = 1 << 12
N_SOFT_BULLISH = 1 << 13
N_SOFT_BEARISH = 1 << 14
# ====================================================>
# =========== SUPERVISER MODEL
# ====================================================>
def create_state_record(label, value):
global RECORD_STATS
global SIGNALS_LOG
if RECORD_STATS:
SIGNALS_LOG[label] = value
def clear_state_records():
global RECORD_STATS
global SIGNALS_LOG
SIGNALS_LOG = {}
def simple_log(*message, log_level=1):
if LOGGING == False:
print("*", end="")
return
if log_level >= LOG_TOLERANCE:
print(*message)
# ====================================================>
# =========== RANDOMIZER
# ====================================================>
class RandomMachine:
def __init__(self, initial_seed, keys_depth=100):
random.seed(initial_seed)
self.keys_depth = keys_depth
self.primary_keys = []
self.refresh_keys()
def refresh_keys(self):
self.primary_keys = list(
random.randint(1, 10 ** 10) for _ in range(self.keys_depth)
)
def update_seed(self):
if len(self.primary_keys) <= 1:
random.seed(self.primary_keys[-1])
self.refresh_keys()
random.seed(self.primary_keys.pop())
def choice(self, options):
self.update_seed()
return random.choice(options)
def randint(self, a, b):
self.update_seed()
return random.randint(a, b)
def randrange(self, a, b, step):
self.update_seed()
return random.randrange(a, b, step)
def uniform(self, a, b):
self.update_seed()
return random.uniform(a, b)
def shuffle(self, container):
random.shuffle(container)
# ====================================================>
# =========== META PARAMETERS. SIMPLE//STUPID
# ====================================================>
META_SIZE = 13
meta_params = [1 for _ in range(META_SIZE)]
meta_option = [None for _ in range(META_SIZE)]
meta_cached = {}
MT_HA_WEIGHT = 0
meta_option[MT_HA_WEIGHT] = lambda: RANDOM.choice([1.25, 1.5, 1.75])
meta_params[MT_HA_WEIGHT] = 1
MT_BOILINGER_WEIGHT = 1
meta_option[MT_BOILINGER_WEIGHT] = lambda: RANDOM.choice([1, 1.5, 2, 2.5])
meta_params[MT_BOILINGER_WEIGHT] = 1
MT_EXPLORE_TARGET = 2
meta_option[MT_EXPLORE_TARGET] = lambda: RANDOM.randrange(4, 20, 2)
meta_params[MT_EXPLORE_TARGET] = 1
def generateSLTP():
sl = RANDOM.choice([1.0, 1.5])
tp_dominance = RANDOM.choice([1.0, 1.5])
tp = sl + tp_dominance
return sl, tp
MT_SLTP = 3
meta_option[MT_SLTP] = generateSLTP
meta_params[MT_SLTP] = [1, 1.5]
MT_BOILINGER_PERIOD = 4
meta_option[MT_BOILINGER_PERIOD] = lambda: RANDOM.randrange(8, 32, 4)
meta_params[MT_BOILINGER_PERIOD] = 14
def generateHKCOMP():
red = RANDOM.randint(2, 4)
green = RANDOM.randint(2, 5)
return red, green
# HA condition
MT_HK_ROW = 5
meta_option[MT_HK_ROW] = generateHKCOMP
meta_params[MT_HK_ROW] = [3, 5]
# SAR ACCELERATION
MT_SAR_ACC = 6
meta_option[MT_SAR_ACC] = lambda: RANDOM.uniform(0, 0.7)
meta_params[MT_SAR_ACC] = 0
MT_EXPLORE_SAMPLES = 7
meta_option[MT_EXPLORE_SAMPLES] = lambda: RANDOM.randrange(6, 40, 2)
meta_params[MT_EXPLORE_SAMPLES] = 10
MT_INDICATORS_DEPTH = 8
meta_option[MT_INDICATORS_DEPTH] = lambda: RANDOM.choice([3, 4, 5, 6])
meta_params[MT_INDICATORS_DEPTH] = 3
MT_WINDOW = 9
meta_option[MT_WINDOW] = lambda: RANDOM.randrange(0, 350, 20)
meta_params[MT_WINDOW] = 0
MT_CONFL_DEPTH = 10
meta_option[MT_CONFL_DEPTH] = lambda: RANDOM.choice([1, 2, 3, 4, 5])
meta_params[MT_CONFL_DEPTH] = 3
MT_CONFL_TRESH = 11
meta_option[MT_CONFL_TRESH] = lambda: RANDOM.choice([0.5, 0.6, 0.7, 0.8])
meta_params[MT_CONFL_TRESH] = 0.7
MT_SET_IGNORE = 12
meta_option[MT_SET_IGNORE] = lambda: RANDOM.choice([True, False])
meta_params[MT_SET_IGNORE] = False
meta_duplicate = meta_params[:]
# ====================================================>
# =========== DRAWER
# ====================================================>
# TODO rewrite it completely
# ====================================================>
def make_image_snapshot(variant_candles, variant_indicators, variant_orders, p1, p2):
def drawSquareInZone(image, zone, x1, y1, x2, y2, col):
try:
X = zone[0]
Y = zone[1]
dx = zone[2] - X
dy = zone[3] - Y
X1 = int(X + dx * x1)
Y1 = int(Y + dy * y1)
X2 = int(X + dx * x2)
Y2 = int(Y + dy * y2)
cv.rectangle(image, (Y1, X1), (Y2, X2), col, -1)
except Exception:
pass
def drawLineInZone(image, zone, x1, y1, x2, y2, col, thickness=1):
try:
X = zone[0]
Y = zone[1]
dx = zone[2] - X
dy = zone[3] - Y
X1 = int(X + dx * x1)
Y1 = int(Y + dy * y1)
X2 = int(X + dx * x2)
Y2 = int(Y + dy * y2)
cv.line(image, (Y1, X1), (Y2, X2), col, thickness)
except Exception:
pass
def getCandleCol(candle):
if candle.green:
# col = (94,224,13)
col = (0, 255, 0)
elif candle.red:
# col = (32,40,224)
col = (0, 0, 255)
elif candle.bad:
col = (0, 255, 255)
else:
col = (255, 255, 255)
return col
def fitTozone(val, minP, maxP):
candleRelative = (val - minP) / (maxP - minP)
return candleRelative
def drawCandle(image, zone, candle, minP, maxP, p1, p2):
i = candle.index - p1
col = getCandleCol(candle)
_o, _c, _h, _l = candle.ochl()
oline = fitTozone(_o, minP, maxP)
cline = fitTozone(_c, minP, maxP)
lwick = fitTozone(_l, minP, maxP)
hwick = fitTozone(_h, minP, maxP)
if candle.isLong() or candle.isShort():
slline = fitTozone(candle.SL, minP, maxP)
tpline = fitTozone(candle.TP, minP, maxP)
slWick = lwick if abs(slline - lwick) < abs(slline - hwick) else hwick
tpWick = lwick if abs(tpline - lwick) < abs(tpline - hwick) else hwick
drawSquareInZone(
img,
zone,
1 - tpWick,
(i + 0.5 + 0.1) / depth,
1 - tpline + 0.005,
(i + 0.1) / depth,
(180 - 10, 58 - 10, 59 - 10),
)
drawSquareInZone(
img,
zone,
1 - slWick,
(i + 0.5 + 0.1) / depth,
1 - slline + 0.005,
(i + 0.1) / depth,
(25 - 10, 120 - 10, 180 - 10),
)
if not candle.hitTP is None:
hitInd = candle.hitTP - p1
drawSquareInZone(
img,
zone,
1 - tpline - 0.005,
(i + 0.5 + 0.7) / depth,
1 - tpline + 0.005,
(hitInd + 0.5) / depth,
(180 - 10, 58 - 10, 59 - 10),
)
if not candle.hitSL is None:
hitInd = candle.hitSL - p1
drawSquareInZone(
img,
zone,
1 - slline - 0.005,
(i + 0.5 - 0.7) / depth,
1 - slline + 0.005,
(hitInd + 0.5) / depth,
(25 - 10, 120 - 10, 180 - 10),
)
if not candle.channel:
drawLineInZone(
img,
zone,
1 - lwick,
(i + 0.5) / depth,
1 - hwick,
(i + 0.5) / depth,
col,
)
drawSquareInZone(
img,
zone,
1 - cline,
(i + 0.5 - 0.35) / depth,
1 - oline,
(i + 0.5 + 0.35) / depth,
col,
)
else:
drawSquareInZone(
img,
zone,
1 - lwick + 0.005,
(i + 0.5 - 0.35) / depth,
1 - lwick - 0.005,
(i + 0.5 + 0.35) / depth,
col,
)
drawSquareInZone(
img,
zone,
1 - hwick + 0.005,
(i + 0.5 - 0.35) / depth,
1 - hwick - 0.005,
(i + 0.5 + 0.35) / depth,
col,
)
def drawOrder(image, zone, order, minP, maxP, p1, p2):
i = order.origin_index - p1
col = (255, 255, 255)
lwick = fitTozone(order.lower_border, minP, maxP)
hwick = fitTozone(order.higher_border, minP, maxP)
drawLineInZone(
img,
zone,
1 - lwick,
(i + 0.5) / depth,
1 - hwick,
(i + 0.5) / depth,
col,
thickness=3,
)
if not order.close_idx is None:
i2 = order.close_idx - p1
med_wick = (lwick + hwick) / 2
drawLineInZone(
img,
zone,
1 - hwick,
(i + 0.5) / depth,
1 - hwick,
(i2 + 0.5) / depth,
col,
thickness=1,
)
drawLineInZone(
img,
zone,
1 - lwick,
(i + 0.5) / depth,
1 - lwick,
(i2 + 0.5) / depth,
col,
thickness=1,
)
drawLineInZone(
img,
zone,
1 - med_wick,
(i + 0.5) / depth,
1 - med_wick,
(i2 + 0.5) / depth,
col,
thickness=1,
)
def drawIndicatorSegment(
image, zone, indicator, I1, I2, minP, maxP, i1, i2, primaryColor=None
):
i_diff = i2 - i1
i_fit_1 = i1 + i_diff * 0.35
i_fit_2 = i1 + i_diff * 0.75
col = (255, 255, 255)
val1 = fitTozone(indicator.getValue(I1), minP, maxP)
val2 = fitTozone(indicator.getValue(I2), minP, maxP)
if indicator.isBearish(I1) or indicator.isBearish(I2):
col = (0, 0, 255)
elif indicator.isBullish(I2) or indicator.isBullish(I2):
col = (0, 255, 0)
elif indicator.isSoftBullish(I2) or indicator.isSoftBullish(I2):
col = (0, 100, 0)
elif indicator.isSoftBearish(I2) or indicator.isSoftBearish(I2):
col = (0, 0, 100)
elif indicator.isBad(I1) or indicator.isBad(I2):
col = (0, 255, 255)
thickness = 2
drawLineInZone(
img,
zone,
1 - val1,
(i_fit_1 + 0.5) / depth,
1 - val2,
(i_fit_2 + 0.5) / depth,
col,
thickness,
)
if indicator.isLong(I1):
drawLineInZone(
img,
zone,
1 - val1,
(i1 + 0.5) / depth,
0,
(i1 + 0.5) / depth,
(0, 180, 0),
thickness,
)
if indicator.isShort(I1):
drawLineInZone(
img,
zone,
1 - val1,
(i1 + 0.5) / depth,
1,
(i1 + 0.5) / depth,
(0, 0, 180),
thickness,
)
def minMaxOfZone(variant_candles, variant_indicators, p1, p2):
if len(variant_candles) == 0 and len(variant_indicators) == 0:
return 0, 0
if len(variant_candles) > 0:
minP = variant_candles[0].ofIdx(p1).l
maxP = variant_candles[0].ofIdx(p1).h
else:
minP = variant_indicators[0].getValue(p1)
maxP = variant_indicators[0].getValue(p1)
for candle_sequence in variant_candles:
minP = min(candle_sequence.minL(p1, p2), minP)
maxP = max(candle_sequence.maxH(p1, p2), maxP)
for indicator in variant_indicators:
# simple_log(indicator.values)
minP = min(indicator.minV(p1, p2), minP)
maxP = max(indicator.maxV(p1, p2), maxP)
rangeP = maxP - minP
return minP, maxP
def drawCandles(img, variant_candles, zone, minV, maxV, p1, p2):
for candle in variant_candles[p1:p2]:
drawCandle(img, zone, candle, minV, maxV, p1, p2)
def drawOrders(img, order_list, zone, minV, maxV, p1, p2):
for order in order_list:
drawOrder(img, zone, order, minV, maxV, p1, p2)
def drawIndicator(img, indicator, zone, minV, maxV, p1, p2):
for i1, i2 in zip(range(p1, p2 - 1), range(p1 + 1, p2)):
drawIndicatorSegment(
img,
zone,
indicator,
i1,
i2,
minV,
maxV,
i1 - p1,
i2 - p1,
indicator.primaryColor,
)
def drawLineNet(img, lines_step, H, W):
line_interval = W // lines_step
for line_counter in range(0, line_interval, 1):
line_level = line_counter * lines_step
cv.line(img, (line_level, 0), (line_level, H), (75, 75, 75), 1)
depth = len(variant_candles[0].variant_candles[p1:p2]) + 1
simple_log(f"DRAWING {depth} variant_candles")
PIXELS_PER_CANDLE = 4
H, W = 1500, depth * PIXELS_PER_CANDLE
img = np.zeros((H, W, 3), np.uint8)
zones = []
firstSquare = [20, 20, H - 20, W - 20]
drawSquareInZone(img, firstSquare, 0, 0, 1, 1, (15, 15, 15))
firstZone = []
zones.append(firstSquare)
# sixthSquare = [H/9*8.0-5,20,H, W-20]
# drawSquareInZone(img, sixthSquare, 0,0,1,1,(15,15,15))
# zones.append(sixthSquare)
drawLineNet(img, 75, H, W)
zoneDrawables = [
{
"zone": _,
"variant_candles": [],
"variant_indicators": [],
"variant_orders": [],
"min": 0,
"max": 0,
}
for _ in range(len(zones))
]
for candle_sequence in variant_candles:
zoneDrawables[candle_sequence.section]["variant_candles"].append(
candle_sequence
)
for indicator in variant_indicators:
zoneDrawables[indicator.section]["variant_indicators"].append(indicator)
for order_list in variant_orders:
# HARDCODED
zoneDrawables[0]["variant_orders"].append(order_list)
for drawableSet in zoneDrawables:
drawableSet["min"], drawableSet["max"] = minMaxOfZone(
drawableSet["variant_candles"], drawableSet["variant_indicators"], p1, p2
)
for drawableSet in zoneDrawables:
zoneN = drawableSet["zone"]
minV = drawableSet["min"]
maxV = drawableSet["max"]
for indicator in drawableSet["variant_indicators"]:
drawIndicator(img, indicator, zones[zoneN], minV, maxV, p1, p2)
for candle_sequence in drawableSet["variant_candles"]:
drawCandles(img, candle_sequence, zones[zoneN], minV, maxV, p1, p2)
for order_list in drawableSet["variant_orders"]:
drawOrders(img, order_list, zones[zoneN], minV, maxV, p1, p2)
return img
# ====================================================>
# =========== INDICATORS BASE CLASSES
# ====================================================>
# TODO change min_max_avg to cummulatives
# ====================================================>
class Indicator:
META_WEIGHT_INDEX = None
META_VARIANT1_INDEX = None
META_VARIANT2_INDEX = None
META_VARIANT3_INDEX = None
def __init__(
self,
candle_sequence,
section,
primaryColor=None,
depth=meta_params[MT_INDICATORS_DEPTH],
):
self.section = section
self.candle_sequence = candle_sequence
self.weight = 0
self.variable_1 = 30
self.primaryColor = primaryColor
self.maxValue = None
self.minValue = None
self.averageValue = None
self.initial_idx = 0
self.num_values = 0
self.values = None
self.state = None
self.delta = None
self.acc = None
self.initialize(len(self.candle_sequence))
self.depth = depth
self.restored = False
self.recalculated = False
self.setWeight()
def initialize(self, num_values):
self.num_values = num_values
self.values = np.asarray([0 for _ in range(self.num_values)])
self.state = np.asarray([N_UNDEFINED for _ in range(self.num_values)])
self.delta = np.asarray([0 for _ in range(self.num_values)])
self.acc = np.asarray([0 for _ in range(self.num_values)])
self.avg_value = 0
self.avg_delta = 0
self.avg_acc = 0
self.std_acc = 0
self.std_delta = 0
self.std_value = 0
def prepare_weights_selector(self):
return lambda: RANDOM.choice([1])
def prepare_variant_1_selector(self):
return lambda: RANDOM.randrange(14, 60, 10)
def record_cache(self):
return False
def try_restore_simple_cached(self):
return False
def resolve_meta_weight(self):
global meta_params
if self.__class__.META_WEIGHT_INDEX is None:
global meta_option
global META_SIZE
global meta_duplicate
self.__class__.META_WEIGHT_INDEX = len(meta_option)
meta_option.append(self.prepare_weights_selector())
meta_params.append(meta_option[self.__class__.META_WEIGHT_INDEX]())
META_SIZE = len(meta_option)
meta_duplicate = meta_params[:]
return meta_params[self.__class__.META_WEIGHT_INDEX]
def resolve_variable_1(self):
global meta_params
if self.__class__.META_VARIANT1_INDEX is None:
global meta_option
global META_SIZE
global meta_duplicate
self.__class__.META_VARIANT1_INDEX = len(meta_option)
meta_option.append(self.prepare_variant_1_selector())
meta_params.append(meta_option[self.__class__.META_VARIANT1_INDEX]())
META_SIZE = len(meta_option)
meta_duplicate = meta_params[:]
return meta_params[self.__class__.META_VARIANT1_INDEX]
def get_delta(self, idx):
return self.delta[idx]
def get_acc(self, idx):
return self.acc[idx]
def isLong(self, idx):
return self.state[idx] & N_LONG
def isShort(self, idx):
return self.state[idx] & N_SHORT
def isTrap(self, idx):
return self.state[idx] & N_TRAP
def isEntry(self, idx):
return self.isLong(idx) or self.isShort(idx)
def goLong(self, idx):
self.state[idx] |= N_LONG
def goShort(self, idx):
self.state[idx] |= N_SHORT
def markBearish(self, idx):
self.state[idx] |= N_BEARISH
def markBullish(self, idx):
self.state[idx] |= N_BULLISH
def isBearish(self, idx):
return self.state[idx] & N_BEARISH
def isBullish(self, idx):
return self.state[idx] & N_BULLISH
def isSoftBullish(self, idx):
return self.state[idx] & N_SOFT_BULLISH
def isSoftBearish(self, idx):
return self.state[idx] & N_SOFT_BEARISH
def markBad(self, idx):
self.state[idx] |= N_BAD
def isBad(self, idx):
return self.state[idx] & N_BAD
def getValue(self, idx):
return self.values[idx]
def getAcc(self, idx):
return self.acc[idx]
def getDt(self, idx):
return self.delta[idx]
def calculate(self):
pass
def setWeight(self):
self.weight = self.resolve_meta_weight()
def calculate_dynamics(self):
self.delta = np.diff(self.values, prepend=self.values[0])
self.acc = np.diff(self.delta, prepend=self.delta[0])
self.avg_value = np.mean(self.values)
self.avg_delta = np.mean(self.delta)
self.avg_acc = np.mean(self.acc)
self.std_val = np.std(self.values)
self.std_del = np.std(self.delta)
self.std_acc = np.std(self.acc)
def maxV(self, p1, p2):
return self.values[p1:p2].max()
def minV(self, p1, p2):
return self.values[p1:p2].min()
def maxAbs(self):
return self.values.max()
def minAbs(self):
return self.values.min()
def average(self, p1, p2):
return self.values[p1:p2].mean()
def median(self, p1, p2):
return np.median(self.values[p1:p2])
def std(self, p1, p2):
return np.std(self.values[p1:p2])
class VOLUME(Indicator):
def __init__(self, *args, **kw):
super().__init__(*args, **kw)
def calculate(self):
arrClose = []
self.values = np.asarray(list(_.v for _ in self.candle_sequence))
self.calculate_dynamics()
# ====================================================>
# =========== CANDLES MODEL
# ====================================================>
class Candle:
def __init__(self, o, c, h, l, v, sequence, index):
self.o = o
self.c = c
self.h = h
self.l = l
self.v = v
self.green = self.c >= self.o
self.red = self.c < self.o
self.upperWick = self.h > max(self.o, self.c)
self.lowerWick = self.l < min(self.o, self.c)
self.longEntry = False
self.shortEntry = False
self.bearish = False
self.bullish = False
self.bad = False
self.sequence = sequence
self.index = index
self.SL = None
self.TP = None
self.hitSL = None
self.hitTP = None
self.ignore = False
self.channel = False
def ochl(self):
return self.o, self.c, self.h, self.l
def prevC(self):
return self.sequence.variant_candles[self.index - 1]
def nextC(self):
return self.sequence.variant_candles[self.index + 1]
def goLong(self):
self.longEntry = True
def goShort(self):
self.shortEntry = True
def isLong(self):
return self.longEntry
def isShort(self):
return self.shortEntry
def isEntry(self):
return self.isLong() or self.isShort()
def markBearish(self):
self.bearish = True
def markBullish(self):
self.bullish = True
def markBad(self):
self.bad = True
def isBad(self):
return self.bad
# TEST 0001
def setIgnore(self):
self.ignore = True
self.longEntry = False
self.shortEntry = False
self.bearish = False
self.bullish = False
self.SL = 0
self.TP = 0
self.hitSL = None
self.hitTP = None
self.green = False
self.red = False
class CandleSequence:
def __init__(self, section):
self.section = section
self.variant_candles = []
self.weight = 1
self.o_cached = None
self.c_cached = None
self.h_cached = None
self.l_cached = None
self.v_cached = None
def addCandle(self, candle):
self.variant_candles.append(candle)
def cache_values(self):
self.o_cached = np.asarray([candle.o for candle in self.variant_candles])
self.c_cached = np.asarray([candle.c for candle in self.variant_candles])
self.h_cached = np.asarray([candle.h for candle in self.variant_candles])
self.l_cached = np.asarray([candle.l for candle in self.variant_candles])
self.v_cached = np.asarray([candle.v for candle in self.variant_candles])
def _toArrayIndex(self, index):
return index - self.variant_candles[0].index
def ofIdx(self, idx):
return self.variant_candles[self._toArrayIndex(idx)]
def maxO(self, p1, p2):
return self.o_cached[p1:p2].max()
def minO(self, p1, p2):
return self.o_cached[p1:p2].min()
def maxC(self, p1, p2):
return self.c_cached[p1:p2].max()
def minC(self, p1, p2):
return self.c_cached[p1:p2].min()
def maxH(self, p1, p2):
return self.h_cached[p1:p2].max()
def minH(self, p1, p2):
return self.h_cached[p1:p2].min()
def maxL(self, p1, p2):
return self.l_cached[p1:p2].max()
def minL(self, p1, p2):
return self.l_cached[p1:p2].min()
def append(self, value):
self.variant_candles.append(value)
def ofRange(self, p1, p2):
return self.variant_candles[p1:p2]
def setWeight(self, weight):
self.weight = weight
def __len__(self):
return len(self.variant_candles)
def __getitem__(self, key):
return self.variant_candles[key]