class PriceSignals:
def __init__(self):
self.m = Metrics()
def Type1_buy_point_MACD_bullish_divergence(self, data):
closePrices = [float(x['Close']) for x in data]
macd_all = self.m.macd_all(closePrices)
if macd_all == None:
return False
macd_fast = macd_all['fast']
macd_slow = macd_all['slow']
macd_histo = macd_all['histo']
if max(macd_slow[0], macd_fast[0], macd_histo[0]) > 0:
return False
if macd_histo[0] < macd_histo[1]:
return False
#现在判断是否有"下跌-调整-下跌":
openPrices = [x['Open'] for x in data]
sr = self.m.support_and_resistance(openPrices, closePrices, 20)
sr_price = sr['price']
lower_bound = min(sr_price[0], sr_price[1]) * 0.95
upper_bound = max(sr_price[0], sr_price[1]) * 1.05
if closePrices[0] > lower_bound:
return False
idx = 2
while idx < len(sr_price):
if sr_price[idx] < lower_bound:
return False
if sr_price[idx] > upper_bound:
break
idx += 1
if idx == len(sr_price) or sr_price[idx] < lower_bound:
return False
sr_idx = sr['idx']
#phase1 is the first 'down' phase
phase1_right_idx = sr_idx[idx-1]
phase1_left_idx = sr_idx[idx]
phase1_min_idx = numpy.argmin(closePrices[phase1_right_idx: phase1_left_idx])
#phase3 is the second (last) 'down' phase
phase3_right_idx = 0
phase3_left_idx = sr_idx[0]
phase3_min_idx = numpy.argmin(closePrices[phase3_right_idx: phase3_left_idx])
# price should be new low but macd_fast should not
if closePrices[phase3_min_idx] > closePrices[phase1_min_idx]:
return False
if macd_fast[phase3_min_idx] < macd_fast[phase1_min_idx]:
return False
return True
"""
思想:
在长期的股价低迷之后,出现了突破,短期均线超过长期均线.但是随后出现调整.
这个调整伴随着缩量, 可能是shorter trap,可以介入.
因为交易量数据的不准确, 程序中不读取交易量
算法:
下列条件必须首先满足, 否则立即返回False:
最新价格比上一个价格是下降的;
短期均线(5)在中期均线(10)和长期均线(20)之下
# 找到三个区间:
区间1: 从最近的交易开始,往回找,直到短期均线位于中期和长期均线上方
区间2: 从区间1的分界日开始往回找,往回找,直到短期均线位于中期和长期均线下方
区间3: 从区间2的分界日开始,往回找,直到短期均线和中期均线均位于长期均线之上
# 计算区间2的最高价,区间3的最低价, 以及区间3开始的价格(成为最初价)
区间3的最低价必须低于最初价20%以上,否则不构成"股价低迷"
区间2的最高价必须高于区间3最低价的5%,否则不成为突破
如果当前价低于区间2的最高价15%，则是下跌而不是回调
如果当前价格是三个区间的最低价，则是下跌而不是回调
# 如果进行到这里,则可以看成是满足条件
"""
def Type2_buy_point_pullback_after_breakthrough(self, closePrices):
ema_short = self.m.ema(closePrices, 5)
ema_mid = self.m.ema(closePrices, 10)
ema_long = self.m.ema(closePrices, 20)
if closePrices[0] > closePrices[1]:
return False
if ema_short[0] > min(ema_mid[0], ema_long[0]):
return False
dsize = len(closePrices)
pos = []
idx = 0
#locate end of section 1
while idx < dsize and ema_short[idx] < max(ema_mid[idx], ema_long[idx]):
idx += 1
if idx == dsize:
return False
pos.append(idx)
#locate end of section 2
while idx < dsize and ema_short[idx] > min(ema_mid[idx], ema_long[idx]):
idx += 1
if idx == dsize:
return False
pos.append(idx)
#locate end of section 3
while idx < dsize and (ema_short[idx] < max(ema_mid[idx], ema_long[idx]) or ema_mid[idx] < ema_long[idx]) :
idx += 1
if idx == dsize:
return False
pos.append(idx)
init_price = closePrices[pos[2]]
low_section3 = min(closePrices[pos[1]:pos[2]])
high_section2 = max(closePrices[pos[0]:pos[1]])
if low_section3 > init_price * 0.8 or high_section2 < low_section3 * 1.05 or \
closePrices[0] < high_section2 * 0.85:
return False
if closePrices[0] < min(closePrices[1:pos[2]]):
return False
return True
#MACD底部背驰
def MACD_Bottom_reversal(self, closePrices):
macd_all = self.m.macd_all(closePrices)
if macd_all == None:
return False
macd_fast = macd_all['fast']
macd_slow = macd_all['slow']
macd_histo = macd_all['histo']
if max(macd_slow[0], macd_fast[0], macd_histo[0]) > 0:
return False
if macd_histo[0] < macd_histo[1]:
return False
# MACD底部背离已经出现:
# 底部背离：依次出现金叉，死叉，金叉。虽然这些叉出现时的价格越来越低，但是对应的macd_fast值越来越高
# 按照第一类买点的定律，出现第二个金叉时，已经错过了最低买点，所以要求是:
# 最近一次出现了金叉，死叉，当前
pos = 0
while pos < len(closePrices) and macd_histo[pos]<0:
pos += 1
if pos == len(closePrices):
return False
dead_cross = pos
pos += 1
while pos < len(closePrices) and macd_histo[pos]>0:
pos += 1
if pos == len(closePrices):
return False
gold_cross = pos
"""
print(str([0, dead_cross, gold_cross]))
print(str([closePrices[0], closePrices[dead_cross], closePrices[gold_cross]]))
print(str([macd_fast[0], macd_fast[dead_cross], macd_fast[gold_cross]]))
"""
#价格更低，但是macd没有更低，这就是一种背离
if not (closePrices[0] < closePrices[gold_cross] and\
macd_fast[0] > macd_fast[gold_cross]):
return False
return True
#Note that the 'Open' data of the current day is not reliable, so let's not depend on it
def Bottom_Up(self, openPrices, closePrices, lows, highs):
#previous bar must be a nagative bar
if not (closePrices[1] < openPrices[1]):
return False
#current price must be higher than previous close
if not (closePrices[0] > closePrices[1]):
return False
#if any recent close price is higher than ema_5 or ema_10, pass
#a low price is necessary for profitable reversal
ema_5 = Metrics().ema(closePrices, 5)
ema_10 = Metrics().ema(closePrices, 10)
for idx in range(1, 3):
if closePrices[idx] > min(ema_5[idx], ema_10[idx]):
return False
# look back 300 bars for support and resistence, very old bars are not reliable
sr = self.m.support_and_resistance(openPrices[:300], closePrices[:300])
myrange = {'min': lows[1], 'max':max(closePrices[0], highs[1])}
foundSupport = False
supportIdx = -1
for i in range(len(sr['price'])):
price = sr['price'][i]
if price > myrange['max']:
continue
if price < myrange['min']:
return False
idx = sr['idx'][i]
# 如果之前跌破了找到的这个支撑点，那么这个支撑点已经作废了
if min(closePrices[2:idx]) < price:
return False
foundSupport = True
supportIdx = idx
break
if not foundSupport:
return False
prevMax = max(closePrices[1:supportIdx])
#print(str([supportIdx, prevMax, closePrices[0]]))
if closePrices[0] > prevMax * 0.9:
return False
return True
def New_High(self, data):
closePrices = [x['Close'] for x in data]
highs = [x['High'] for x in data]
if len(closePrices) < 20:
return False
prev_high = max(highs[1:])
cur_price = closePrices[0]
#print(str([cur_price, prev_high]))
if cur_price < prev_high:
return False
ema_5 = self.m.ema(closePrices, 5)
ema_10 = self.m.ema(closePrices, 10)
# if price increased fast, skip
limit = 1.02
if (cur_price > min(ema_5[0], closePrices[1]) * limit) or\
(ema_5[0] > ema_10[0] * limit):
return False
#用支撑线阻力线的位置来判断,价格是否在某区间徘徊
#如果价格一路上升,是不会有支撑和阻力线的
openPrices = [x['Open'] for x in data]
sr = self.m.support_and_resistance(openPrices, closePrices)
sr_price = sr['price']
lower_bound = cur_price * 0.95
count = 0
for p in range(len(sr_price)):
if sr_price[p] > lower_bound:
count += 1
if count < 2:
return False
rsi = self.m.rsi(closePrices)
if max(rsi[:5]) > 70:
return False
macd_all = self.m.macd_all(closePrices)
macd_fast = macd_all['fast']
sr_idx = sr['idx']
if macd_fast[0] < macd_fast[sr_idx[0]]:
#这是MACD背离
return False
return True
#这个版本是: "整数新高"
def New_High_Old(self, closePrices, highs):
if len(closePrices) < 20:
return False
prev_high = max(highs[1:])
cur_price = closePrices[0]
if cur_price < prev_high:
return False
cur_price *= 1.02 #if cur_price is 2% short, print it too, so I can watch it
if cur_price < 10:
price_threshold = 1
elif cur_price < 100:
price_threshold = 10
else:
price_threshold = 100
if int(closePrices[0] / price_threshold) == int(prev_high / price_threshold):
return False
return True
def __init__(self):
self.m = Metrics()
