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dosangyoon
2021-10-26 00:55:33 +09:00
parent 7ad0cfece2
commit 6025cddcc4
2 changed files with 35 additions and 91 deletions
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111
hts/BuySellChecker.py
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@@ -272,24 +272,26 @@ class BuySellChecker:
pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
if pre_slow < 0 and 0 < now_slow:
if data["slow_k"][i] <= 20:
if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.1:
if data["slow_k"][i] <= 35:
if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
if data['avg3'][i] < data['avg2'][i]:
if data["open"][i] < data["close"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
else:
pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
if pre_slow < 0 and pre_slow < now_slow and -0.15 < now_slow:
if data["slow_k"][i] <= 20:
if data["slow_k"][i] <= 30:
if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
if data["slow_k"][i - 1] < data["slow_d"][i - 1] and data["slow_d"][i] < data["slow_k"][i]:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
if data['avg3'][i] < data['avg2'][i]:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
#############################
### STOCHASTIC weight 분석 ###
@@ -305,72 +307,6 @@ class BuySellChecker:
return buy, weight, sell
def getPriceAndWeight3(self, data, i):
buy, weight, sell = -1, -1, -1
################
### sell 분석 ###
################
# 1. 볼린져밴드 상단이 최고와 종가 사이 아래에 있는 경우 매도한다.
if (data["high"][i] - data["close"][i]) / 2 + data["close"][i] > data["upper"][i]:
sell = data["high"][i]
if data["slow_k"][i] >= 85:
if data["slow_d"][i - 1] < data["slow_k"][i - 1] and data["slow_k"][i] < data["slow_d"][i]:
sell = data["high"][i]
# 3. 2시 이후에는 최고가가 볼린져밴드 상단 위에 있으면 매도한다.
if i > 300 and data["high"][i] > data["upper"][i]:
sell = data["high"][i]
##########################
### STOCHASTIC buy 분석 ###
##########################
if i < 40:
pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
if data["slow_d"][i - 2] > data["slow_d"][i - 1] and data["slow_d"][i - 1] < data["slow_d"][i]:
if abs(data["slow_d"][i]-data["slow_k"][i]) < abs(data["slow_d"][i-1]-data["slow_k"][i-1]):
if now_slow < 0.15:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
if data["slow_k"][i-1] < data["slow_d"][i-1] and data["slow_d"][i] < data["slow_k"][i]:
if abs(now_slow) < 0.001:
if now_slow < 0.15:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
else:
if i > 60:
print (1)
pre_slow = data["slow_k"][i - 1] / data["slow_d"][i - 1] - 1
now_slow = data["slow_k"][i] / data["slow_d"][i] - 1
if pre_slow < 0 and pre_slow < now_slow and -0.15 < now_slow:
if data["slow_k"][i] <= 20:
if (data["close"][i] - data["lower"][i]) / (data["upper"][i] - data["lower"][i]) < 0.35:
if data["close"][i] < data["avg5"][i]:
buy = data["close"][i]
else:
buy = data["low"][i]
#############################
### STOCHASTIC weight 분석 ###
#############################
if data["slow_k"][i] in (0, 1, 2, 3):
weight = 1
if data["slow_k"][i] in (4, 5, 6, 7, 8):
weight = 1
elif data["slow_k"][i] in (9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20):
weight = 1
elif data["slow_k"][i] in (21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35):
weight = 1
return buy, weight, sell
def analyze(self, result):
df = pd.DataFrame(result["close"])
@@ -391,6 +327,8 @@ class BuySellChecker:
avg1 = [item[0] for item in avg1_list]
avg2_list = close_df.rolling(window=2).mean().fillna(close[0]).values.tolist()
avg2 = [item[0] for item in avg2_list]
avg3_list = close_df.rolling(window=3).mean().fillna(close[0]).values.tolist()
avg3 = [item[0] for item in avg3_list]
avg5_list = close_df.rolling(window=5).mean().fillna(close[0]).values.tolist()
avg5 = [item[0] for item in avg5_list]
avg10_list = close_df.rolling(window=10).mean().fillna(close[0]).values.tolist()
@@ -405,6 +343,10 @@ class BuySellChecker:
avg50 = [item[0] for item in avg50_list]
avg60_list = close_df.rolling(window=60).mean().fillna(close[0]).values.tolist()
avg60 = [item[0] for item in avg60_list]
avg120_list = close_df.rolling(window=120).mean().fillna(close[0]).values.tolist()
avg120 = [item[0] for item in avg120_list]
avg240_list = close_df.rolling(window=240).mean().fillna(close[0]).values.tolist()
avg240 = [item[0] for item in avg240_list]
upper, lower = [], []
for i in range(len(upper_df)):
@@ -419,10 +361,9 @@ class BuySellChecker:
STOCK = []
for i in range(len(result["open"])):
STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i],
'high': high[i], 'low': low[i], 'avg5': avg2[i],
'avg20': avg5[i], 'avg60': avg10[i], 'avg120': avg20[i],
'avg240': avg30[i]})
STOCK.append({'volume': vol[i], 'close': close[i], 'open': open[i], 'high': high[i], 'low': low[i],
'avg1': avg1[i],'avg2': avg2[i],'avg3': avg3[i],'avg5': avg5[i],'avg10': avg10[i],
'avg20': avg20[i], 'avg60': avg60[i], 'avg120': avg120[i],'avg240': avg240[i]})
# stochastic 계산
stochastic_df = self.stochastic.apply(pd.DataFrame(STOCK))
@@ -438,11 +379,9 @@ class BuySellChecker:
rsis = rsi_df['rsis'].values.tolist()
temp = {"date": point_temp,
"open": open, "high": high, "low": low, "close": close, "volume": vol,
"upper": upper, "lower": lower,
"avg1": avg1, "avg2": avg2, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg40": avg40, "avg50": avg50, "avg60": avg60,
"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d,
"rsi": rsi, "rsis": rsis}
"open": open, "high": high, "low": low, "close": close, "volume": vol, "upper": upper, "lower": lower,
"avg1": avg1, "avg2": avg2, "avg3": avg3, "avg5": avg5, "avg10": avg10, "avg20": avg20, "avg30": avg30, "avg40": avg40, "avg50": avg50, "avg60": avg60, "avg120": avg120, "avg240": avg240,
"fast_k": fast_k, "slow_k": slow_k, "slow_d": slow_d, "rsi": rsi, "rsis": rsis}
data = pd.DataFrame(temp)
df_final_time = pd.DatetimeIndex(point_temp)
data.index = df_final_time