(MCM/ICM)比特币和黄金组合投资策略的策略代码部分

2022-02-23 约 1018 字预计阅读 3 分钟

前言

美赛论文对应策略代码部分，吐槽一句美赛居然不收代码也是离谱。

论文配套的代码均为本人编写，运行无碍。

感谢组员组长的配合论文撰写。

正文

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import numpy as np
import pandas as pd
import re,math
import matplotlib.pyplot as plt


np.set_printoptions(suppress=True)
B = pd.read_csv(r'B.csv') # B
H = pd.read_csv(r'H.csv') # H
Times = pd.read_csv(r'022.csv') # Time
B = B.set_index("Unnamed: 0")
H = H.set_index("Unnamed: 0")
Times = Times.set_index("Unnamed: 0")
# 先向前取值填充，再先后取值填充
BH = pd.merge(H.iloc[:,0:2], B.iloc[:,0:2], how='outer',on='0').sort_values('0',ascending=True)
BH = pd.merge(BH, H.iloc[:,0:3:2], how="left", on=["0"])
BH = pd.merge(BH, B.iloc[:,0:3:2], how="left", on=["0"]).fillna(0)

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H

	0	1	2
Unnamed: 0
0	76	-1	1145.90
1	154	1	1281.85
2	171	-1	1257.40
3	233	1	1282.30
4	247	-1	1333.10
5	308	1	1291.85
6	324	-1	1264.55
7	532	1	1223.00
8	551	-1	1203.25
9	608	1	1312.40
10	628	-1	1285.85
11	685	1	1280.95
12	703	-1	1431.40
13	760	1	1503.10
14	781	-1	1490.60
15	839	1	1567.85
16	856	-1	1578.25
17	912	1	1682.05
18	931	-1	1737.95
19	988	1	2031.15
20	1008	-1	1928.45

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m = 10000
h = 0
b = 0
p = 0
q = 0
control_list = []
for i in range(len(Times)):
    x1 = 0
    x2 = 0
    j = 0
    t = 0
    if i <= 9:
        control_list.append([i,0,m,h,j,b,t,x1,x2])
        continue
    else:
        if i not in list(np.array(BH['0'])):
            control_list.append([i,0,m,h,j,b,t,x1,x2])
            continue
    if np.array(BH[BH['0'].isin([str(i)])]['1_x'])[0] != 0: # H
#         if np.array(BH[BH['0'].isin([str(i)])]['2_y'])[0] == 0: # B
        if np.array(BH[BH['0'].isin([str(i)])]['1_x'])[0] < 0: # 买
            j = np.array(H[H['0'].isin([i])]['2'])[0]
            print(j)
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1#(x1/(x1+x2))
            q = m*x2#(x2/(x1+x2))
            h = (p-0.01*p)/j
            p = 0
            m = p + q
            control_list.append([i,11,m,h,j,b,0,x1,x2])
#             print(m)
        if np.array(BH[BH['0'].isin([str(i)])]['1_x'])[0] > 0: # 卖
            j = np.array(H[H['0'].isin([i])]['2'])[0]
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1
            q = m*x2
            m = h*j-h*j*0.01+p+q
            h = 0
            control_list.append([i,-11,m,h,j,b,0,x1,x2])
#             print(m)
            
#             if np.array(BH[BH['0'].isin([str(i)])]['2_x'])[0] == 0: # H
    if np.array(BH[BH['0'].isin([str(i)])]['1_y'])[0] != 0: # B
        if np.array(BH[BH['0'].isin([str(i)])]['1_y'])[0] < 0: # 买
            t = np.array(B[B['0'].isin([i])]['2'])[0]
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1#(x1/(x1+x2))
            q = m*x2#(x2/(x1+x2))
            b = (q-0.02*q)/t
            q = 0
            m = p + q
            control_list.append([i,22,m,h,0,b,t,x1,x2])
#             print(m)
        elif np.array(BH[BH['0'].isin([str(i)])]['1_y'])[0] > 0: # 卖
            t = np.array(B[B['0'].isin([i])]['2'])[0]
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1
            q = m*x2
            m = b*t-b*t*0.02+p+q
            b = 0
            control_list.append([i,-22,m,h,0,b,t,x1,x2])
#             print(m)

m

1145.9
1257.4
1333.1
1264.55
1203.25
1285.85
1431.4
1490.6
1578.25
1737.95
1928.45





2.935705996261112e-06

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m = 10000
h = 0
b = 0
p = 0
q = 0
control_list = []
for i in range(len(Times)):
    x1 = 0
    x2 = 0
    j = 0
    t = 0
    if i <= 9:
        control_list.append([i,0,m,h,j,b,t,x1,x2])
        continue
    else:
        if i not in list(np.array(BH['0'])):
            control_list.append([i,0,m,h,j,b,t,x1,x2])
            continue
    if np.array(BH[BH['0'].isin([str(i)])]['1_x'])[0] != 0: # H
#         if np.array(BH[BH['0'].isin([str(i)])]['2_y'])[0] == 0: # B
        if np.array(BH[BH['0'].isin([str(i)])]['1_x'])[0] < 0: # 买
            j = np.array(H[H['0'].isin([i])]['2'])[0]
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1#(x1/(x1+x2))
            q = m*x2#(x2/(x1+x2))
            h = (p-0.01*p)/j
            p = 0
            m = p + q
            control_list.append([i,11,m,h,j,b,t,x1,x2])
#             print(m)
        if np.array(BH[BH['0'].isin([str(i)])]['1_x'])[0] > 0: # 卖
            j = np.array(H[H['0'].isin([i])]['2'])[0]
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1
            q = m*x2
            m = h*j-h*j*0.01+p+q
            h = 0
            control_list.append([i,-11,m,h,j,b,t,x1,x2])
#             print(m)
            
#             if np.array(BH[BH['0'].isin([str(i)])]['2_x'])[0] == 0: # H
    if np.array(BH[BH['0'].isin([str(i)])]['1_y'])[0] != 0: # B
        if np.array(BH[BH['0'].isin([str(i)])]['1_y'])[0] < 0: # 买
            t = np.array(B[B['0'].isin([i])]['2'])[0]
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1#(x1/(x1+x2))
            q = m*x2#(x2/(x1+x2))
            b = (q-0.02*q)/t
            q = 0
            m = p + q
            control_list.append([i,22,m,h,j,b,t,x1,x2])
#             print(m)
        elif np.array(BH[BH['0'].isin([str(i)])]['1_y'])[0] > 0: # 卖
            t = np.array(B[B['0'].isin([i])]['2'])[0]
            x1 = np.array(Times.iloc[[i]]['0'])[0]
            x2 = np.array(Times.iloc[[i]]['1'])[0]
            p = m*x1
            q = m*x2
            m = b*t-b*t*0.02+p+q
            b = 0
            control_list.append([i,-22,m,h,j,b,t,x1,x2])
#             print(m)

m

1
2


data_data = pd.DataFrame(control_list,columns=["day","process","USD","H","Hprice","B","Bprice","Hx1","Bx2"])#.to_csv("Process.csv")
data_data

	day	process	USD	H	Hprice	B	Bprice	Hx1	Bx2
0	0	0	10000.000000	0.000000	0.0	0.0	0.0	0.0	0.0
1	1	0	10000.000000	0.000000	0.0	0.0	0.0	0.0	0.0
2	2	0	10000.000000	0.000000	0.0	0.0	0.0	0.0	0.0
3	3	0	10000.000000	0.000000	0.0	0.0	0.0	0.0	0.0
4	4	0	10000.000000	0.000000	0.0	0.0	0.0	0.0	0.0
...	...	...	...	...	...	...	...	...	...
1820	1819	0	0.000003	5.729175	0.0	0.0	0.0	0.0	0.0
1821	1820	0	0.000003	5.729175	0.0	0.0	0.0	0.0	0.0
1822	1821	0	0.000003	5.729175	0.0	0.0	0.0	0.0	0.0
1823	1822	0	0.000003	5.729175	0.0	0.0	0.0	0.0	0.0
1824	1823	0	0.000003	5.729175	0.0	0.0	0.0	0.0	0.0

1825 rows × 9 columns

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ttp = []
price = pd.read_csv(r'price.csv', index_col=["Unnamed: 0"])
ttp.append(np.array(data_data["USD"])+np.array(data_data["H"])*np.array(price["USD (PM)"])[1:]+np.array(data_data["H"])*np.array(price["USD (PM)"])[1:])
ttp.append(np.array(data_data["H"])*np.array(price["USD (PM)"])[1:])
ttp.append(np.array(data_data["B"])*np.array(price["Value"])[1:])
# ttp.append(price["Value"])
# ttp.append(price["USD (PM)"])

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pd.DataFrame(ttp).T

	0	1	2
0	10000.000000	0.000000	0.0
1	10000.000000	0.000000	0.0
2	10000.000000	0.000000	0.0
3	10000.000000	0.000000	0.0
4	10000.000000	0.000000	0.0
...	...	...	...
1820	20872.528860	10436.264429	0.0
1821	20649.663969	10324.831983	0.0
1822	20464.611630	10232.305813	0.0
1823	20490.392915	10245.196456	0.0
1824	20563.153432	10281.576715	0.0

1825 rows × 3 columns

目录

(MCM/ICM)比特币和黄金组合投资策略的策略代码部分

前言

正文