def get_bpdata(path, loc, classies):
norm = MinMaxScaler()
data = pd.DataFrame(pd.read_csv(path, header=None))
arrdata = np.array(data)
print(arrdata[loc])
maxV = max(np.array(arrdata[:, 2:6]).reshape(-1, 1))
minV = min(np.array(arrdata[:, 2:6]).reshape(-1, 1))
normarrdata = norm.fit_transform(arrdata[:, 2:6])
adata = []
alable = []
normadata = []
normlable = []
if len(classies) == 1: # len(arrdata)-11:
for z in range(2, 6):
for y in range(10):
adata.extend([arrdata[loc + y][z]])
normadata.extend([normarrdata[loc + y][z - 2]])
alable.extend([arrdata[loc + 11][z]])
normlable.extend([normarrdata[loc + 11][z - 2]])
else:
print("classiss长度不为1，结果为", classies)
return adata, normadata, alable, normlable, maxV, minV
def processdata(way,max_price,min_price,max_volume,min_volume):
data = pd.DataFrame(pd.read_csv(way, header=0))
norm = MinMaxScaler() # 为了归一化数据
arrdata = np.array(data)
# datetemp = list(arrdata[:, 0])
# date = list(set(datetemp))
# date.sort(key=datetemp.index) # 集合数据按照原来顺序进行排序
# 将所有数据按照30分钟进行切分，一天四个小时，240分钟
list_data = []
for i in range(0, len(data), 30):
list_data.append(arrdata[i:i + 30, :]) # 数据的日期、时间、开、高、低、收、成交量、成交额
# print(len(list_data))
results = []
# 切分的数据按照十个30分钟一组垂直组合
for j in range(len(list_data) - 11):
r = np.zeros([100, 10]) # 下跌，一行代表0.01
g = np.zeros([100, 10]) # 上涨
b = np.zeros([100, 10]) # 成交量
# if j >= 10:
# break
temp = np.concatenate((list_data[j], list_data[j + 1], list_data[j + 2], list_data[j + 3], list_data[j + 4],
list_data[j + 5], list_data[j + 6], list_data[j + 7], list_data[j + 8],
list_data[j + 9]), axis=0) # 共10天，垂直组合
# 组合后，归一化价格，绘制图像
price = np.array(temp[:, 3:7], dtype='float')
volume = np.array(temp[:, 7], dtype='int')
# 这里放的十天的数据
# norm_price = norm.fit_transform(price.reshape(-1, 1)) # 归一化价格
# norm_volume = norm.fit_transform(volume.reshape(-1, 1)) # 归一化成交量
# norm_price = norm_price.reshape(-1, 4)
norm_price= normalize(price.reshape(-1, 1),max_price,min_price) # 归一化价格
norm_volume = normalize(volume.reshape(-1, 1),max_volume,min_volume) # 归一化成交量
norm_price = norm_price.reshape(-1, 4)
# 切分归一化后的价格
days_normprice = []
days_normvolume = []
days_volume = []
for k in range(0, len(norm_price), 30):
days_normprice.append(norm_price[k:k + 30, :])
days_normvolume.append(norm_volume[k:k + 30, :])
days_volume.append(volume[k:k + 30])
# 按天绘制图像
for l in range(len(days_normprice)):
norm_open = float(days_normprice[l][0, 0]) # 开盘价
norm_close = float(days_normprice[l][-1, 3]) # 收盘价
norm_high = float(max(days_normprice[l][:, 1])) # 最高价
norm_low = float(min(days_normprice[l][:, 2])) # 最低价
print('open', norm_open, 'high', norm_high, 'low', norm_low, 'close', norm_close)
loc_open = round(norm_open / 0.01) # 开盘价的位置 ,四舍五入
loc_high = round(norm_high / 0.01) # 开盘价的位置
loc_low = round(norm_low / 0.01) # 开盘价的位置
loc_close = round(norm_close / 0.01) # 开盘价的位置
body = loc_close - loc_open
upper_shadow = loc_high - max([loc_open, loc_close])
lower_shadow = min(loc_open, loc_close) - loc_low
if loc_close == 100:
loc_close = 99
if loc_high == 100:
loc_high = 99
if loc_open == 100:
loc_open = 99
if loc_low == 100:
loc_low = 99
print('body', body, "upper_shadow", upper_shadow, "lower_shadow", lower_shadow)
if body >= 0: # 上涨，g矩阵改变
g[loc_close:loc_high, l] = 0.5 # 上影线
g[loc_open:loc_close, l] = 1 # 实体
g[loc_low:loc_open, l] = 0.5 # 下影线
if loc_open == loc_close:
g[loc_open, l] = 1 # 实体
# print("g",g)
else: # 下跌,body<0
r[loc_open:loc_high, l] = 0.5 # 上影线
r[loc_close:loc_open, l] = 1 # 实体
r[loc_low:loc_close, l] = 0.5 # 下影线
if loc_open == loc_close:
r[loc_open, l] = 1 # 实体
# print("r", r)
# 成交量改变b矩阵
for m in range(len(days_normprice[l])):
begin_loc = int(round(days_normprice[l][m, 2] / 0.01))
end_loc = int(round(days_normprice[l][m, 1] / 0.01))
if begin_loc == 100:
begin_loc=99
if end_loc == 100:
end_loc = 99
# now_volume = days_normvolume[l][m]
now_volume = days_volume[l][m]
length = end_loc - begin_loc
if length ==0:
b[begin_loc, l] = round(now_volume / 1) + b[begin_loc,l]
else:
b[begin_loc:end_loc, l] = round(now_volume / length) + b[begin_loc:end_loc, l] # 这一价格区间的成交量被均匀分布在这一区间
# print('b',b)
#print("最大成交量", max(max(b.reshape(1, -1))))
b = norm.fit_transform(b.reshape(-1, 1)) # 归一化成交量
b = b.reshape(100, 10)
results.append([r,g,b])
return results
def processdata(way, way3):
data = pd.DataFrame(pd.read_csv(way, header=0))
sum_data = pd.DataFrame(pd.read_csv(way3, header=0))
norm = MinMaxScaler() # 为了归一化数据
arrdata = np.array(data)
sum_arrdata = np.array(sum_data)
# 将所有数据按照一30分钟进行切分
list_data = []
sum_list_data = []
for i in range(0, len(data), 30):
list_data.append(arrdata[i:i +
30, :]) #数据的 市场代码,合约代码,时间,开,高,低,收,成交量,成交额,持仓量
for i in range(0, len(sum_data), 30):
sum_list_data.append(
sum_arrdata[i:i + 30, :]) # 数据的 市场代码,合约代码,时间,开,高,低,收,成交量,成交额,持仓量
print(len(list_data))
print(len(sum_list_data))
results = []
# 切分的数据按照十个30分钟一组垂直组合
for j in range(len(sum_list_data) - 11):
r = np.zeros([100, 10]) # 下跌，一行代表0.01
g = np.zeros([100, 10]) # 上涨
b = np.zeros([100, 10]) # 成交量
# if j >= 20:
# break
temp = np.concatenate(
(sum_list_data[j], sum_list_data[j + 1], sum_list_data[j + 2],
sum_list_data[j + 3], sum_list_data[j + 4], sum_list_data[j + 5],
sum_list_data[j + 6], sum_list_data[j + 7], sum_list_data[j + 8],
sum_list_data[j + 9]),
axis=0) # 共10天，垂直组合
# 组合后，归一化价格，绘制图像
price = np.array(temp[:, 3:7], dtype='float')
volume = np.array(temp[:, 7], dtype='int')
# 这里放的十天的数据
# norm_price = norm.fit_transform(price.reshape(-1, 1)) # 归一化价格
# norm_volume = norm.fit_transform(volume.reshape(-1, 1)) # 归一化成交量
# norm_price = norm_price.reshape(-1, 4)
norm_price, max_price, min_price = normalize(price.reshape(-1,
1)) # 归一化价格
norm_volume, max_volume, min_volume = normalize(volume.reshape(
-1, 1)) # 归一化成交量
norm_price = norm_price.reshape(-1, 4)
# 切分归一化后的价格
days_normprice = []
days_normvolume = []
days_volume = []
for k in range(0, len(norm_price), 30):
days_normprice.append(norm_price[k:k + 30, :])
days_normvolume.append(norm_volume[k:k + 30, :])
days_volume.append(volume[k:k + 30])
# 按天绘制图像
for l in range(len(days_normprice)):
norm_open = float(days_normprice[l][0, 0]) # 开盘价
norm_close = float(days_normprice[l][-1, 3]) # 收盘价
norm_high = float(max(days_normprice[l][:, 1])) # 最高价
norm_low = float(min(days_normprice[l][:, 2])) # 最低价
print('open', norm_open, 'high', norm_high, 'low', norm_low,
'close', norm_close)
loc_open = round(norm_open / 0.01) # 开盘价的位置 ,四舍五入
loc_high = round(norm_high / 0.01) # 开盘价的位置
loc_low = round(norm_low / 0.01) # 开盘价的位置
loc_close = round(norm_close / 0.01) # 开盘价的位置
body = loc_close - loc_open
upper_shadow = loc_high - max([loc_open, loc_close])
lower_shadow = min(loc_open, loc_close) - loc_low
if loc_close == 100:
loc_close = 99
if loc_high == 100:
loc_high = 99
if loc_open == 100:
loc_open = 99
if loc_low == 100:
loc_low = 99
print('body', body, "upper_shadow", upper_shadow, "lower_shadow",
lower_shadow)
if body >= 0: # 上涨，g矩阵改变
g[loc_close:loc_high, l] = 0.5 # 上影线
g[loc_open:loc_close, l] = 1 # 实体
g[loc_low:loc_open, l] = 0.5 # 下影线
if loc_open == loc_close:
g[loc_open, l] = 1 # 实体
# print("g",g)
else: # 下跌,body<0
r[loc_open:loc_high, l] = 0.5 # 上影线
r[loc_close:loc_open, l] = 1 # 实体
r[loc_low:loc_close, l] = 0.5 # 下影线
if loc_open == loc_close:
r[loc_open, l] = 1 # 实体
# print("r", r)
# 成交量改变b矩阵
for m in range(len(days_normprice[l])):
begin_loc = int(round(days_normprice[l][m, 2] / 0.01))
end_loc = int(round(days_normprice[l][m, 1] / 0.01))
if begin_loc == 100:
begin_loc = 99
if end_loc == 100:
end_loc = 99
# now_volume = days_normvolume[l][m]
now_volume = days_volume[l][m]
length = end_loc - begin_loc
if length == 0:
b[begin_loc, l] = round(now_volume / 1) + b[begin_loc, l]
else:
b[begin_loc:end_loc, l] = round(
now_volume / length) + b[begin_loc:end_loc,
l] # 这一价格区间的成交量被均匀分布在这一区间
# print('b',b)
#print("最大成交量", max(max(b.reshape(1, -1))))
b = norm.fit_transform(b.reshape(-1, 1)) # 归一化成交量
b = b.reshape(100, 10)
#将RGB转化为灰度图像
M = np.dstack([r, g, b]) * 255
im = Image.fromarray(np.uint8(M))
im = im.convert('L')
imdata = im.getdata()
M = np.array(imdata).reshape((100, 10))
# print('imshape', np.array(imdata).shape)
# plt.imshow(M, origin='lower')
# plt.imshow(im, origin='lower', cmap='gray')
# plt.show()
#results.append([r,g,b])
results.append([M])
return np.array(
results[:len(list_data)]), max_price, min_price, max_volume, min_volume