def handle_duan(fenxing_list: List[Fenxing], pre_duan_state='yi'):
state = fenxing_list[0].state
# 1笔区间
bi1_start = fenxing_list[0].kdata
bi1_end = fenxing_list[1].kdata
# 3笔区间
bi3_start = fenxing_list[2].kdata
bi3_end = fenxing_list[3].kdata
if state == 'bi_ding':
# 向下段,下-上-下
# 第一笔区间
range1 = (bi1_end['low'], bi1_start['high'])
# 第三笔区间
range3 = (bi3_end['low'], bi3_start['high'])
# 1,3有重叠，认为第一个段出现
if intersect(range1, range3):
return 'down'
else:
# 向上段，上-下-上
# 第一笔区间
range1 = (bi1_start['low'], bi1_end['high'])
# 第三笔区间
range3 = (bi3_start['low'], bi3_end['high'])
# 1,3有重叠，认为第一个段出现
if intersect(range1, range3):
return 'up'
return pre_duan_state
def handle_duan(fenxing_list: List[Fenxing], pre_duan_state="yi"):
state = fenxing_list[0].state
# 1笔区间
bi1_start = fenxing_list[0].kdata
bi1_end = fenxing_list[1].kdata
# 3笔区间
bi3_start = fenxing_list[2].kdata
bi3_end = fenxing_list[3].kdata
if state == "bi_ding":
# 向下段,下-上-下
# 第一笔区间
range1 = (bi1_end["low"], bi1_start["high"])
# 第三笔区间
range3 = (bi3_end["low"], bi3_start["high"])
# 1,3有重叠，认为第一个段出现
if intersect(range1, range3):
return "down"
else:
# 向上段，上-下-上
# 第一笔区间
range1 = (bi1_start["low"], bi1_end["high"])
# 第三笔区间
range3 = (bi3_start["low"], bi3_end["high"])
# 1,3有重叠，认为第一个段出现
if intersect(range1, range3):
return "up"
return pre_duan_state
def handle_zhongshu(points: list,
acc_df,
end_index,
zhongshu_col="zhongshu",
zhongshu_change_col="zhongshu_change"):
zhongshu = None
zhongshu_change = None
interval = None
if len(points) == 4:
x1 = points[0][0]
x2 = points[3][0]
interval = points[3][2] - points[0][2]
if points[0][1] < points[1][1]:
# 向下段
range = intersect((points[0][1], points[1][1]),
(points[2][1], points[3][1]))
if range:
y1, y2 = range
# 记录中枢
zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2)
zhongshu_change = abs(y1 - y2) / y1
acc_df.loc[end_index, zhongshu_col] = zhongshu
acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change
points = points[-1:]
else:
points = points[1:]
else:
# 向上段
range = intersect((points[1][1], points[0][1]),
(points[3][1], points[2][1]))
if range:
y1, y2 = range
# 记录中枢
zhongshu = Rect(x0=x1, x1=x2, y0=y1, y1=y2)
zhongshu_change = abs(y1 - y2) / y1
acc_df.loc[end_index, zhongshu_col] = zhongshu
acc_df.loc[end_index, zhongshu_change_col] = zhongshu_change
points = points[-1:]
else:
points = points[1:]
return points, zhongshu, zhongshu_change, interval
def test_intersect():
a = (1, 2)
b = (1.5, 3)
assert intersect(a, b) == (1.5, 2)
assert intersect(b, a) == (1.5, 2)
a = (1, 2)
b = (3, 4)
assert intersect(a, b) == None
assert intersect(b, a) == None
a = (1, 4)
b = (2, 3)
assert intersect(a, b) == (2, 3)
assert intersect(b, a) == (2, 3)
def acc_one(self, entity_id, df: pd.DataFrame, acc_df: pd.DataFrame,
state: dict) -> (pd.DataFrame, dict):
self.logger.info(f'acc_one:{entity_id}')
if pd_is_not_null(acc_df):
df = df[df.index > acc_df.index[-1]]
if pd_is_not_null(df):
self.logger.info(f'compute from {df.iloc[0]["timestamp"]}')
# 遍历的开始位置
start_index = len(acc_df)
acc_df = pd.concat([acc_df, df])
zen_state = state
acc_df = acc_df.reset_index(drop=True)
else:
self.logger.info('no need to compute')
return acc_df, state
else:
acc_df = df
# 笔的底
acc_df['bi_di'] = False
# 笔的顶
acc_df['bi_ding'] = False
# 记录笔顶/底分型的值，bi_di取low,bi_ding取high,其他为None,绘图时取有值的连线即为 笔
acc_df['bi_value'] = np.NAN
# 记录临时分型，不变
acc_df['tmp_ding'] = False
acc_df['tmp_di'] = False
# 分型的力度
acc_df['fenxing_power'] = np.NAN
acc_df['duan_state'] = 'yi'
# 段的底
acc_df['duan_di'] = False
# 段的顶
acc_df['duan_ding'] = False
# 记录段顶/底的值，为duan_di时取low,为duan_ding时取high,其他为None,绘图时取有值的连线即为 段
acc_df['duan_value'] = np.NAN
# 记录在确定中枢的最后一个段的终点x1，值为Rect(x0,y0,x1,y1)
acc_df['zhongshu'] = np.NAN
acc_df = acc_df.reset_index(drop=True)
zen_state = ZenState(
dict(fenxing_list=[],
direction=None,
can_fenxing=None,
can_fenxing_index=None,
opposite_count=0,
current_duan_state='yi',
duans=[],
pre_bi=None,
pre_duan=None))
zen_state.fenxing_list: List[Fenxing] = []
# 取前11条k线，至多出现一个顶分型+底分型
# 注:只是一种方便的确定第一个分型的办法，有了第一个分型，后面的处理就比较统一
# start_index 为遍历开始的位置
# direction为一个确定分型后的方向，即顶分型后为:down，底分型后为:up
fenxing, start_index, direction = handle_first_fenxing(acc_df,
step=11)
if not fenxing:
return None, None
zen_state.fenxing_list.append(fenxing)
zen_state.direction = direction
# list of (timestamp,value)
zen_state.duans = []
pre_kdata = acc_df.iloc[start_index - 1]
pre_index = start_index - 1
tmp_direction = zen_state.direction
for index, kdata in acc_df.iloc[start_index:].iterrows():
# print(f'timestamp: {kdata.timestamp}')
# 临时方向
tmp_direction = get_direction(kdata,
pre_kdata,
current=tmp_direction)
# 处理包含关系
handle_including(one_df=acc_df,
index=index,
kdata=kdata,
pre_index=pre_index,
pre_kdata=pre_kdata,
tmp_direction=tmp_direction)
# 根据方向，寻找对应的分型 和 段
if zen_state.direction == Direction.up:
tmp_fenxing_col = 'tmp_ding'
fenxing_col = 'bi_ding'
else:
tmp_fenxing_col = 'tmp_di'
fenxing_col = 'bi_di'
# 方向一致，延续中
if tmp_direction == zen_state.direction:
zen_state.opposite_count = 0
# 反向，寻找反 分型
else:
zen_state.opposite_count = zen_state.opposite_count + 1
# 第一次反向
if zen_state.opposite_count == 1:
acc_df.loc[pre_index, tmp_fenxing_col] = True
acc_df.loc[pre_index, 'fenxing_power'] = fenxing_power(
acc_df.loc[pre_index - 1],
pre_kdata,
kdata,
fenxing=tmp_fenxing_col)
if pd_is_not_null(zen_state.can_fenxing):
# 候选底分型
if tmp_direction == Direction.up:
# 取小的
if pre_kdata['low'] <= zen_state.can_fenxing['low']:
zen_state.can_fenxing = pre_kdata
zen_state.can_fenxing_index = pre_index
# 候选顶分型
else:
# 取大的
if pre_kdata['high'] >= zen_state.can_fenxing[
'high']:
zen_state.can_fenxing = pre_kdata
zen_state.can_fenxing_index = pre_index
else:
zen_state.can_fenxing = pre_kdata
zen_state.can_fenxing_index = pre_index
# 分型确立
if pd_is_not_null(zen_state.can_fenxing):
if zen_state.opposite_count >= 4 or (
index - zen_state.can_fenxing_index >= 8):
acc_df.loc[zen_state.can_fenxing_index,
fenxing_col] = True
# 记录笔的值
if fenxing_col == 'bi_ding':
bi_value = acc_df.loc[zen_state.can_fenxing_index,
'high']
else:
bi_value = acc_df.loc[zen_state.can_fenxing_index,
'low']
acc_df.loc[zen_state.can_fenxing_index,
'bi_value'] = bi_value
zen_state.pre_bi = (zen_state.can_fenxing_index,
bi_value)
zen_state.opposite_count = 0
zen_state.direction = zen_state.direction.opposite()
zen_state.can_fenxing = None
# 确定第一个段
if zen_state.fenxing_list != None:
zen_state.fenxing_list.append(
Fenxing(state=fenxing_col,
kdata=acc_df.loc[
zen_state.can_fenxing_index,
['open', 'close', 'high', 'low']],
index=zen_state.can_fenxing_index))
if len(zen_state.fenxing_list) == 4:
duan_state = handle_duan(
fenxing_list=zen_state.fenxing_list,
pre_duan_state=zen_state.current_duan_state
)
change = duan_state != zen_state.current_duan_state
if change:
zen_state.current_duan_state = duan_state
# 确定状态
acc_df.loc[
zen_state.fenxing_list[0].
index:zen_state.fenxing_list[-1].index,
'duan_state'] = zen_state.current_duan_state
duan_index = zen_state.fenxing_list[
0].index
if zen_state.current_duan_state == 'up':
acc_df.loc[duan_index,
'duan_di'] = True
duan_value = acc_df.loc[duan_index,
'low']
else:
duan_index = zen_state.fenxing_list[
0].index
acc_df.loc[duan_index,
'duan_ding'] = True
duan_value = acc_df.loc[duan_index,
'high']
# 记录段的值
acc_df.loc[duan_index,
'duan_value'] = duan_value
# 记录用于计算中枢的段
zen_state.duans.append(
(acc_df.loc[duan_index,
'timestamp'], duan_value))
# 计算中枢
if len(zen_state.duans) == 4:
x1 = zen_state.duans[0][0]
x2 = zen_state.duans[3][0]
if zen_state.duans[0][
1] < zen_state.duans[1][1]:
# 向下段
range = intersect(
(zen_state.duans[0][1],
zen_state.duans[1][1]),
(zen_state.duans[2][1],
zen_state.duans[3][1]))
if range:
y1, y2 = range
# 记录中枢
acc_df.loc[duan_index,
'zhongshu'] = Rect(
x0=x1,
x1=x2,
y0=y1,
y1=y2)
zen_state.duans = zen_state.duans[
-1:]
else:
zen_state.duans = zen_state.duans[
1:]
else:
# 向上段
range = intersect(
(zen_state.duans[1][1],
zen_state.duans[0][1]),
(zen_state.duans[3][1],
zen_state.duans[2][1]))
if range:
y1, y2 = range
# 记录中枢
acc_df.loc[duan_index,
'zhongshu'] = Rect(
x0=x1,
x1=x2,
y0=y1,
y1=y2)
zen_state.duans = zen_state.duans[
-1:]
else:
zen_state.duans = zen_state.duans[
1:]
# 只留最后一个
zen_state.fenxing_list = zen_state.fenxing_list[
-1:]
else:
# 保持之前的状态并踢出候选
acc_df.loc[
zen_state.fenxing_list[0].index,
'duan_state'] = zen_state.current_duan_state
zen_state.fenxing_list = zen_state.fenxing_list[
1:]
pre_kdata = kdata
pre_index = index
acc_df = acc_df.set_index('timestamp', drop=False)
return acc_df, zen_state
def do_compute(self):
super().do_compute()
one_df = self.factor_df
# annotation_df format:
# value flag color
# entity_id timestamp
# 处理分型
bi_ding = one_df[one_df.bi_ding][['timestamp', 'high']]
bi_di = one_df[one_df.bi_di][['timestamp', 'low']]
df1 = bi_ding.rename(columns={"high": "value"})
df1['flag'] = '顶分型'
df2 = bi_di.rename(columns={"low": "value"})
df2['flag'] = '底分型'
flag_df: pd.DataFrame = pd.concat([df1, df2])
flag_df = flag_df.sort_values(by=['timestamp'])
flag_df['entity_id'] = self.entity_ids[0]
flag_df = flag_df.set_index(['entity_id', 'timestamp'])
# 处理段
up = one_df[one_df.duan_di][['timestamp', 'low']]
down = one_df[one_df.duan_ding][['timestamp', 'high']]
df1 = up.rename(columns={"low": "value"})
df2 = down.rename(columns={"high": "value"})
duan_df: pd.DataFrame = pd.concat([df1, df2])
duan_df = duan_df.sort_values(by=['timestamp'])
duan_df['entity_id'] = self.entity_ids[0]
duan_df = duan_df.set_index(['entity_id', 'timestamp'])
# 处理中枢
rects: List[Rect] = []
# list of (timestamp,value)
duans = []
for index, item in duan_df.iterrows():
duans.append((index[1], item.value))
if len(duans) == 4:
x1 = duans[0][0]
x2 = duans[3][0]
if duans[0][1] < duans[1][1]:
# 向下段
range = intersect((duans[0][1], duans[1][1]),
(duans[2][1], duans[3][1]))
if range:
y1, y2 = range
else:
duans = duans[1:]
continue
else:
# 向上段
range = intersect((duans[1][1], duans[0][1]),
(duans[3][1], duans[2][1]))
if range:
y1, y2 = range
else:
duans = duans[1:]
continue
rects.append(Rect(x0=x1, x1=x2, y0=y1, y1=y2))
duans = duans[-1:]
self.fenxing_value_df = flag_df
self.duan_value_df = duan_df
self.zhongshu_rects = rects
def transform_one(self, entity_id, df: pd.DataFrame) -> pd.DataFrame:
# 记录段区间
if entity_id not in self.entity_duan_intervals:
self.entity_duan_intervals[entity_id] = []
df = df.reset_index(drop=True)
# 笔的底
df['bi_di'] = False
# 笔的顶
df['bi_ding'] = False
# 记录临时分型，不变
df['tmp_ding'] = False
df['tmp_di'] = False
df['duan_state'] = 'yi'
# 段的底
df['duan_di'] = False
# 段的顶
df['duan_ding'] = False
# 记录段顶/底的值，为duan_di时取low,为duan_ding时取high,其他为None,绘图时取有值的连线即为 段
df['duan_value'] = np.NAN
# 记录在确定中枢的最后一个段的终点x1，值为Rect(x0,y0,x1,y1)
df['zhongshu'] = None
fenxing_list: List[Fenxing] = []
# 取前11条k线，至多出现一个顶分型+底分型
# 注:只是一种方便的确定第一个分型的办法，有了第一个分型，后面的处理就比较统一
# start_index 为遍历开始的位置
# direction为一个确定分型后的方向，即顶分型后为:down，底分型后为:up
fenxing, start_index, direction = handle_first_fenxing(df, step=11)
fenxing_list.append(fenxing)
# 临时方向
tmp_direction = direction
# 候选分型(candidate)
can_fenxing = None
can_fenxing_index = None
# 正向count
count = 0
# 反方向count
opposite_count = 0
# 目前段的方向
current_duan_state = 'yi'
pre_kdata = df.iloc[start_index - 1]
pre_index = start_index - 1
# list of (timestamp,value)
duans = []
for index, kdata in df.iloc[start_index:].iterrows():
# print(f'timestamp: {kdata.timestamp}')
# 临时方向
tmp_direction = get_direction(kdata,
pre_kdata,
current=tmp_direction)
# 处理包含关系
handle_including(one_df=df,
index=index,
kdata=kdata,
pre_index=pre_index,
pre_kdata=pre_kdata,
tmp_direction=tmp_direction)
# 根据方向，寻找对应的分型 和 段
if direction == Direction.up:
tmp_fenxing_col = 'tmp_ding'
fenxing_col = 'bi_ding'
else:
tmp_fenxing_col = 'tmp_di'
fenxing_col = 'bi_di'
# 方向一致，延续中
if tmp_direction == direction:
opposite_count = 0
# 反向，寻找反 分型
else:
opposite_count = opposite_count + 1
# 第一次反向
if opposite_count == 1:
df.loc[pre_index, tmp_fenxing_col] = True
if pd_is_not_null(can_fenxing):
# 候选底分型
if tmp_direction == Direction.up:
# 取小的
if pre_kdata['low'] <= can_fenxing['low']:
can_fenxing = pre_kdata
can_fenxing_index = pre_index
# 候选顶分型
else:
# 取大的
if pre_kdata['high'] >= can_fenxing['high']:
can_fenxing = pre_kdata
can_fenxing_index = pre_index
else:
can_fenxing = pre_kdata
can_fenxing_index = pre_index
# 分型确立
if pd_is_not_null(can_fenxing):
if opposite_count >= 4 or (index - can_fenxing_index >= 8):
df.loc[can_fenxing_index, fenxing_col] = True
# 记录笔的值
if fenxing_col == 'bi_ding':
df.loc[can_fenxing_index,
'bi_value'] = df.loc[can_fenxing_index,
'high']
else:
df.loc[can_fenxing_index,
'bi_value'] = df.loc[can_fenxing_index,
'low']
opposite_count = 0
direction = direction.opposite()
can_fenxing = None
# 确定第一个段
if fenxing_list != None:
fenxing_list.append(
Fenxing(state=fenxing_col,
kdata=df.loc[can_fenxing_index],
index=can_fenxing_index))
if len(fenxing_list) == 4:
duan_state = handle_duan(
fenxing_list=fenxing_list,
pre_duan_state=current_duan_state)
change = duan_state != current_duan_state
if change:
current_duan_state = duan_state
# 确定状态
df.loc[fenxing_list[0].
index:fenxing_list[-1].index,
'duan_state'] = current_duan_state
duan_index = fenxing_list[0].index
if current_duan_state == 'up':
df.loc[duan_index, 'duan_di'] = True
duan_value = df.loc[duan_index, 'low']
else:
duan_index = fenxing_list[0].index
df.loc[duan_index, 'duan_ding'] = True
duan_value = df.loc[duan_index, 'high']
# 记录段的值
df.loc[duan_index,
'duan_value'] = duan_value
# 记录用于计算中枢的段
duans.append(
(df.loc[duan_index,
'timestamp'], duan_value))
# 计算中枢
if len(duans) == 4:
x1 = duans[0][0]
x2 = duans[3][0]
if duans[0][1] < duans[1][1]:
# 向下段
range = intersect(
(duans[0][1], duans[1][1]),
(duans[2][1], duans[3][1]))
if range:
y1, y2 = range
# 记录中枢
df.loc[duan_index,
'zhongshu'] = Rect(
x0=x1,
x1=x2,
y0=y1,
y1=y2)
duans = duans[-1:]
else:
duans = duans[1:]
else:
# 向上段
range = intersect(
(duans[1][1], duans[0][1]),
(duans[3][1], duans[2][1]))
if range:
y1, y2 = range
# 记录中枢
df.loc[duan_index,
'zhongshu'] = Rect(
x0=x1,
x1=x2,
y0=y1,
y1=y2)
duans = duans[-1:]
else:
duans = duans[1:]
# 只留最后一个
fenxing_list = fenxing_list[-1:]
else:
# 保持之前的状态并踢出候选
df.loc[fenxing_list[0].index,
'duan_state'] = current_duan_state
fenxing_list = fenxing_list[1:]
pre_kdata = kdata
pre_index = index
df = df.set_index('timestamp')
return df
df2 = down.rename(columns={"high": "value"})
duan_df: pd.DataFrame = pd.concat([df1, df2])
duan_df = duan_df.sort_values(by=['timestamp'])
duan_df['entity_id'] = 'stock_sz_000338'
duan_df = duan_df.set_index(['entity_id', 'timestamp'])
# 处理中枢
rects = []
duans = []
for index, item in duan_df.iterrows():
if len(duans) == 4:
x = duans[0][0]
x1 = duans[3][0]
if duans[0][1] < duans[1][1]:
y, y1 = intersect((duans[0][1], duans[1][1]), (duans[2][1], duans[3][1]))
else:
y, y1 = intersect((duans[1][1], duans[0][1]), (duans[3][1], duans[2][1]))
rects.append(((x, y), (x1, y1)))
duans = []
else:
duans.append((index[1], item.value))
drawer = Drawer(main_df=df, factor_df_list=[flag_df[['value']], duan_df], annotation_df=flag_df)
fig = drawer.draw_kline(show=False)
for rect in rects:
fig.add_shape(type="rect",
x0=rect[0][0], y0=rect[0][1], x1=rect[1][0], y1=rect[1][1],
line=dict(
color="RoyalBlue",
