Mapa central de la opinión pública trasladada en base a un artículo de gran tamaño Se ha añadido un mapa de varios centros Me gustaría conocer la opinión pública. Por cierto, aquí está la estrategia de personalización:https://www.fmz.cn/market-offer/199
'''
start: 2020-10-1 00:00:00
end: 2020-10-15 16:00:00
period: 1h
basePeriod: 1h
exchanges: [{"eid":"OKEX","currency":"BTC_USDT","stocks":1}]
'''
import pandas as pd
from fmz import * # 导入所有FMZ函数
task = VCtx(__doc__) # 初始化
#!pip install --user mplfinance
#import sys
#sys.path.append('/home/quant/.local/lib/python3.6/site-packages')
#import mplfinance# 第三方函数库
import numpy as np
import pandas as pd
import matplotlib as mpl
import matplotlib.pyplot as plt
#import mplfinance as mpf
import matplotlib.patches as patches
import talib
import datetime
import warnings
warnings.filterwarnings("ignore")
def get_k_series():
#
# 获取k线序列，默认为30分钟级别
# 输入：n是级别，单位是分钟
# 输出：pandas, k线序列
n = 1
one_min_data = pd.DataFrame(exchange.GetRecords())
one_min_data = one_min_data.rename(columns={'Time':'date','Open':'open','Close':'close','High':'high','Low':'low'})
one_min_data['date'] = one_min_data['date'].apply(lambda x:_D(x/1000))
one_min_data['Date'] = one_min_data['date'].apply(lambda x:pd.to_datetime(x))
one_min_data.set_index('Date',inplace=True)
#print(one_min_data)
n_min_data = pd.DataFrame()
for i in range(n, len(one_min_data) + 1, n):
interval = one_min_data.iloc[i - n:i]
interval_open = interval.open.iloc[0]
interval_high = max(interval.high)
interval_low = min(interval.low)
interval_date = interval.date
interval_k = pd.DataFrame(interval[-1:]) # 新建DataFrame，否则会报SettingWithCopyWarning
interval_k.open = interval_open
interval_k.high = interval_high
interval_k.low = interval_low
interval_k.date = interval_date
#print(interval_k)
n_min_data = pd.concat([n_min_data, interval_k], axis=0)
n_min_data = n_min_data.reset_index()
#del n_min_data['instrument']
#del n_min_data['index']
#print(n_min_data)
return n_min_data
def get_binary_positions(k_data):
#
# 计算k线序列的二分位值
# 输入：k线序列
# 输出：list, k线序列对应的二分位值
binary_positions = []
for i in range(len(k_data)):
temp_y = (k_data.high[i] + k_data.low[i]) / 2.0
binary_positions.append(temp_y)
return binary_positions
def adjust_by_cintainment(k_data):
#
# 判断k线的包含关系，便于寻找顶分型和底分型
# 输入：k线序列
# 输出：adjusted_k_data, 处理后的k线序列
trend = [0]
adjusted_k_data = pd.DataFrame()
temp_data = k_data[:1]
#print(temp_data)
#return
for i in range(len(k_data)):
#print("处理：",i)
is_equal = temp_data.high.iloc[-1] == k_data.high.iloc[i] and temp_data.low.iloc[-1] == k_data.low.iloc[i] # 第1根等于第2根
# 向右包含
if temp_data.high.iloc[-1] >= k_data.high.iloc[i] and temp_data.low.iloc[-1] <= k_data.low.iloc[i] and not is_equal:
if trend[-1] == -1:
temp_data.high.iloc[-1] = k_data.high.iloc[i]
else:
temp_data.low.iloc[-1] = k_data.low.iloc[i]
# 向左包含
elif temp_data.high.iloc[-1] <= k_data.high.iloc[i] and temp_data.low.iloc[-1] >= k_data.low.iloc[i] and not is_equal:
if trend[-1] == -1:
temp_data.low.iloc[-1] = k_data.low.iloc[i]
else:
temp_data.high.iloc[-1] = k_data.high.iloc[i]
elif is_equal:
trend.append(0)
elif temp_data.high.iloc[-1] > k_data.high.iloc[i] and temp_data.low.iloc[-1] > k_data.low.iloc[i]:
trend.append(-1)
temp_data = k_data[i:i + 1]
elif temp_data.high.iloc[-1] < k_data.high.iloc[i] and temp_data.low.iloc[-1] < k_data.low.iloc[i]:
trend.append(1)
temp_data = k_data[i:i + 1]
#print("处理判断完毕：",i)
#print("调整收盘价和开盘价：",i)
# 调整收盘价和开盘价
if temp_data.open.iloc[-1] > temp_data.close.iloc[-1]:
if temp_data.open.iloc[-1] > temp_data.high.iloc[-1]:
temp_data.open.iloc[-1] = temp_data.high.iloc[-1]
if temp_data.close.iloc[-1] < temp_data.low.iloc[-1]:
temp_data.close.iloc[-1] = temp_data.low.iloc[-1]
else:
if temp_data.open.iloc[-1] < temp_data.low.iloc[-1]:
temp_data.open.iloc[-1] = temp_data.low.iloc[-1]
if temp_data.close.iloc[-1] > temp_data.high.iloc[-1]:
temp_data.close.iloc[-1] = temp_data.high.iloc[-1]
adjusted_data = k_data[i:i + 1]
adjusted_data.open.iloc[-1] = temp_data.open.iloc[-1]
adjusted_data.close.iloc[-1] = temp_data.close.iloc[-1]
adjusted_data.high.iloc[-1] = temp_data.high.iloc[-1]
adjusted_data.low.iloc[-1] = temp_data.low.iloc[-1]
#print("调整收盘价和开盘价完毕：",i)
adjusted_k_data = pd.concat([adjusted_k_data, adjusted_data], axis=0)
return adjusted_k_data
def get_fx(adjusted_k_data):
#
# 寻找顶分型和底分型
# 1）连续分型选择最极端值
# 2）分型之间保证3根k线
# 输入：调整后的k线序列
# 输出：顶分型和底分型的位置
temp_num = 0 # 上一个顶或底的位置
temp_high = 0 # 上一个顶的high值
temp_low = 0 # 上一个底的low值
temp_type = 0 # 上一个记录位置的类型
fx_type = [] # 记录分型点的类型，1为顶分型，-1为底分型
fx_time = [] # 记录分型点的时间
fx_plot = [] # 记录点的数值，为顶分型取high值，为底分型取low值
fx_data = pd.DataFrame() # 记录分型
fx_offset = []
# 加上线段起点
fx_type.append(0)
fx_offset.append(0)
#fx_time.append(adjusted_k_data.index[0].strftime("%Y-%m-%d %H:%M:%S"))
fx_time.append(adjusted_k_data.date[0])
fx_data = pd.concat([fx_data, adjusted_k_data[:1]], axis=0)
fx_plot.append((adjusted_k_data.low[0] + adjusted_k_data.high[0]) / 2)
i = 1
while (i < len(adjusted_k_data) - 1):
top = adjusted_k_data.high[i - 1] <= adjusted_k_data.high[i] \
and adjusted_k_data.high[i] > adjusted_k_data.high[i + 1] # 顶分型
bottom = adjusted_k_data.low[i - 1] >= adjusted_k_data.low[i] \
and adjusted_k_data.low[i] < adjusted_k_data.low[i + 1] # 底分型
if top:
if temp_type == 1:
# 如果上一个分型为顶分型，则进行比较，选取高点更高的分型
if adjusted_k_data.high[i] <= temp_high:
i += 1
else:
temp_high = adjusted_k_data.high[i]
temp_low = adjusted_k_data.low[i]
temp_num = i
temp_type = 1
i += 2 # 两个分型之间至少有3根k线
elif temp_type == -1:
# 如果上一个分型为底分型，则记录上一个分型，用当前分型与后面的分型比较，选取同向更极端的分型
if temp_low >= adjusted_k_data.high[i]:
# 如果上一个底分型的底比当前顶分型的顶高，则跳过当前顶分型。
i += 1
else:
fx_type.append(-1)
#fx_time.append(adjusted_k_data.index[temp_num].strftime("%Y-%m-%d %H:%M:%S"))
fx_time.append(adjusted_k_data.date.iloc[temp_num])
fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)
fx_plot.append(temp_low)
fx_offset.append(temp_num)
temp_high = adjusted_k_data.high[i]
temp_low = adjusted_k_data.low[i]
temp_num = i
temp_type = 1
i += 2 # 两个分型之间至少有3根k线
else:
temp_high = adjusted_k_data.high[i]
temp_low = adjusted_k_data.low[i]
temp_num = i
temp_type = 1
i += 2
elif bottom:
if temp_type == -1:
# 如果上一个分型为底分型，则进行比较，选取低点更低的分型
if adjusted_k_data.low[i] >= temp_low:
i += 1
else:
temp_low = adjusted_k_data.low[i]
temp_high = adjusted_k_data.high[i]
temp_num = i
temp_type = -1
i += 2
elif temp_type == 1:
# 如果上一个分型为顶分型，则记录上一个分型，用当前分型与后面的分型比较，选取同向更极端的分型
if temp_high <= adjusted_k_data.low[i]:
# 如果上一个顶分型的底比当前底分型的底低，则跳过当前底分型。
i += 1
else:
fx_type.append(1)
#fx_time.append(adjusted_k_data.index[temp_num].strftime("%Y-%m-%d %H:%M:%S"))
fx_time.append(adjusted_k_data.date.iloc[temp_num])
fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)
fx_plot.append(temp_high)
fx_offset.append(temp_num)
temp_low = adjusted_k_data.low[i]
temp_high = adjusted_k_data.high[i]
temp_num = i
temp_type = -1
i += 2
else:
temp_low = adjusted_k_data.low[i]
temp_high = adjusted_k_data.high[i]
temp_num = i
temp_type = -1
i += 2
else:
i += 1
# 加上最后一个分型（上面的循环中最后的一个分型并未处理）
if temp_type == -1:
fx_type.append(-1)
#fx_time.append(adjusted_k_data.index[temp_num].strftime("%Y-%m-%d %H:%M:%S"))
fx_time.append(adjusted_k_data.date.iloc[temp_num])
fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)
fx_plot.append(temp_low)
fx_offset.append(temp_num)
elif temp_type == 1:
fx_type.append(1)
#fx_time.append(adjusted_k_data.index[temp_num].strftime("%Y-%m-%d %H:%M:%S"))
fx_time.append(adjusted_k_data.date.iloc[temp_num])
fx_data = pd.concat([fx_data, adjusted_k_data[temp_num:temp_num + 1]], axis=0)
fx_plot.append(temp_high)
fx_offset.append(temp_num)
# 加上线段终点
fx_type.append(0)
fx_offset.append(len(adjusted_k_data) - 1)
#fx_time.append(adjusted_k_data.index[-1].strftime("%Y-%m-%d %H:%M:%S"))
fx_time.append(adjusted_k_data.date.iloc[-1])
fx_data = pd.concat([fx_data, adjusted_k_data[-1:]], axis=0)
fx_plot.append((adjusted_k_data.low.iloc[-1] + adjusted_k_data.high.iloc[-1]) / 2)
return fx_type, fx_time, fx_data, fx_plot, fx_offset
def get_pivot(fx_plot, fx_offset, fx_observe):
#
# 计算最近的中枢
# 注意：一个中枢至少有三笔
# fx_plot 笔的节点股价
# fx_offset 笔的节点时间点（偏移）
# fx_observe 所观测的分型点
if fx_observe < 1:
# 处理边界
right_bound = 0
left_bount = 0
min_high = 0
max_low = 0
pivot_x_interval = [left_bount, right_bound]
pivot_price_interval = [max_low, min_high]
return pivot_x_interval, pivot_price_interval
right_bound = (fx_offset[fx_observe] + fx_offset[fx_observe - 1]) / 2
# 右边界是所观察分型的上一笔中位
left_bount = 0
min_high = 0
max_low = 0
if fx_plot[fx_observe] >= fx_plot[fx_observe - 1]:
# 所观察分型的上一笔是往上的一笔
min_high = fx_plot[fx_observe]
max_low = fx_plot[fx_observe - 1]
else: # 所观察分型的上一笔是往下的一笔
max_low = fx_plot[fx_observe]
min_high = fx_plot[fx_observe - 1]
i = fx_observe - 1
cover = 0 # 记录走势的重叠区，至少为3才能画中枢
while (i >= 1):
if fx_plot[i] >= fx_plot[i - 1]:
# 往上的一笔
if fx_plot[i] < max_low or fx_plot[i - 1] > min_high:
# 已经没有重叠区域了
left_bount = (fx_offset[i] + fx_offset[i + 1]) / 2
break
else:
# 有重叠区域
# 计算更窄的中枢价格区间
cover += 1
min_high = min(fx_plot[i], min_high)
max_low = max(fx_plot[i - 1], max_low)
elif fx_plot[i] < fx_plot[i - 1]:
# 往下的一笔
if fx_plot[i] > min_high or fx_plot[i - 1] < max_low:
# 已经没有重叠区域了
left_bount = (fx_offset[i] + fx_offset[i + 1]) / 2
break
else:
# 有重叠区域
# 计算更窄的中枢价格区间
cover += 3
min_high = min(fx_plot[i - 1], min_high)
max_low = max(fx_plot[i], max_low)
i -= 1
if cover < 3:
# 不满足中枢定义
right_bound = -1
left_bount = -1
min_high = -1
max_low = -1
pivot_x_interval = [left_bount, right_bound]
pivot_price_interval = [max_low, min_high]
return pivot_x_interval, pivot_price_interval,i
def plot_k_series(ax,k_data):
# 画k线
num_of_ticks = len(k_data)
# fig, ax = plt.subplots(figsize=(num_of_ticks, 20))
# fig.subplots_adjust(bottom=0.2)
dates = k_data.date
# print dates
ax.set_xticks(np.linspace(1, num_of_ticks, num_of_ticks))
ax.set_xticklabels(list(dates))
"""
xticks = list(range(0, len(dates), 10)) # 这里设置的是x轴点的位置（40设置的就是间隔了）
xlabels = [dates[x] for x in xticks ] # 这里设置X轴上的点对应在数据集中的值（这里用的数据为totalSeed）
xticks.append(len(dates))
xlabels.append(dates[-1])
ax.set_xticks(xticks)
ax.set_xticklabels(xlabels, rotation=40)
"""
#T.plot(k_data,candlestick=True)
print("绘制K线")
plt.plot(k_data.close)
#print(1)
# mpf.candlestick2_ochl(
# ax,
# list(k_data.open), list(k_data.close), list(k_data.high), list(k_data.low),
# width=0.6, colorup='r', colordown='b', alpha=0.75
# )
#mpf.plot(k_data,type='candle')
plt.grid(True)
plt.setp(plt.gca().get_xticklabels(), rotation=30)
return dates
def plot_lines(ax, fx_plot, fx_offset):
# 绘制笔和线段
# ax 绘图区域
# fx_plot
plt.plot(fx_offset, fx_plot, 'k', lw=1)
plt.plot(fx_offset, fx_plot, 'o')
def plot_pivot(ax, pivot_date_interval, pivot_price_interval):
#
# 绘制中枢
start_point = (pivot_date_interval[0], pivot_price_interval[0])
width = pivot_date_interval[1] - pivot_date_interval[0]
height = pivot_price_interval[1] - pivot_price_interval[0]
print(
"中枢：",
start_point, # (x,y)
width, # width
height, # height
)
plt.gca().add_patch(
patches.Rectangle(
start_point, # (x,y)
width, # width
height, # height
linewidth=8,
edgecolor='g',
facecolor='none'
)
)
return
def plot_all(select_deta=10,price_percent=0.01):#select_deta 中枢最小间隔 price_percent幅度小于某个值
k_series = get_k_series()
kk=k_series
if(len(kk)<10):
print('k线数量不足')
return
fig = plt.figure(figsize=(50, 20))
plt.rcParams.update({'figure.max_open_warning': 0})
ax2= fig.add_subplot(212)
print("处理K线...")
adjusted_k_data = adjust_by_cintainment(k_series)
plot_k_series(ax2,adjusted_k_data) # 调整后的k线图
fx_type, fx_time, fx_data, fx_plot, fx_offset = get_fx(adjusted_k_data)
print (fx_type, fx_time)
plot_lines(ax2, fx_plot, fx_offset)
pivot_x_interval, pivot_price_interval,now_index = None,None,len(fx_offset)-2
#last_pivot_x_interval, last_pivot_price_interval,last_index = get_pivot(fx_plot, fx_offset, now_index)
while now_index >= 1:
pivot_x_interval, pivot_price_interval,now_index = get_pivot(fx_plot, fx_offset, now_index)
if pivot_x_interval[0] == -1:
break
else:
if pivot_x_interval[1] - pivot_x_interval[0] < select_deta:
print("pivot_x_interval[1] - pivot_x_interval[0] < select_deta")
continue
hhv = max(k_series.high[int(pivot_x_interval[0]):int(pivot_x_interval[1])])
llv = min(k_series.low[int(pivot_x_interval[0]):int(pivot_x_interval[1])])
hhv_deta = abs((hhv - pivot_price_interval[1]) / pivot_price_interval[1])
llv_deta = abs((llv - pivot_price_interval[0]) / pivot_price_interval[0])
if (hhv_deta > price_percent or llv_deta > price_percent):
print(" (hhv_deta > 1.0 / price_percent or llv_deta > 1.0 / price_percent)")
continue
plot_pivot(ax2, pivot_x_interval, pivot_price_interval)
plot_all(
select_deta = 2,#中枢最小间隔
price_percent = 0.5#幅度小于某个值
)处理K线... 绘制K线 [0, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 0] ['2020-09-22 16:00:00', '2020-09-22 17:00:00', '2020-09-23 01:00:00', '2020-09-23 07:00:00', '2020-09-23 13:00:00', '2020-09-23 15:00:00', '2020-09-23 17:00:00', '2020-09-23 21:00:00', '2020-09-24 03:00:00', '2020-09-24 05:00:00', '2020-09-24 17:00:00', '2020-09-24 19:00:00', '2020-09-24 23:00:00', '2020-09-25 10:00:00', '2020-09-25 20:00:00', '2020-09-26 01:00:00', '2020-09-26 06:00:00', '2020-09-26 10:00:00', '2020-09-26 18:00:00', '2020-09-26 22:00:00', '2020-09-27 01:00:00', '2020-09-27 12:00:00', '2020-09-27 16:00:00', '2020-09-27 18:00:00', '2020-09-28 02:00:00', '2020-09-28 09:00:00', '2020-09-28 13:00:00', '2020-09-28 15:00:00', '2020-09-28 17:00:00', '2020-09-28 19:00:00', '2020-09-28 21:00:00', '2020-09-29 02:00:00', '2020-09-29 04:00:00', '2020-09-29 06:00:00', '2020-09-29 11:00:00', '2020-09-29 17:00:00', '2020-09-30 00:00:00', '2020-09-30 09:00:00', '2020-09-30 17:00:00', '2020-09-30 20:00:00', '2020-10-01 00:00:00'] 中枢： (151.5, 10681.6) 43.0 30.299999999999272 中枢： (126.0, 10855.3) 22.0 31.80000000000109 中枢： (43.0, 10702.8) 78.0 31.80000000000109 中枢： (27.0, 10270.8) 9.0 70.60000000000036 中枢： (0, 10450.0) 24.0 19.549999999999272 <Figure size 3600x1440 with 1 Axes>
¿Qué es esto?¿Quieres?
La homilíaMe siento bien, pero el código es un poco desordenado, no entiendo el método de la pluma y el centro, voy a estudiar más detenidamente. Además, por favor pregúntame si hay posibilidades de funciones futuras.
Las hierbasY es difícil traducir descripciones basadas en imágenes en código.
abc_quantSegún el centro de cálculo del sector histórico, no hay funciones futuras.