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7 files changed

+129
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lines changed

‎new Season/bank_account_sol.py‎

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import numpy as np
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import matplotlib.pyplot as plt
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def sdf_MC(r0,dr,n,p):
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m = 100000
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x = np.random.random((m,n-1))
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ud = np.where(x<p,dr,-dr)
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R = np.c_[np.zeros((m,1)),ud]
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R = r0 + R.cumsum(axis=1)
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ba = np.exp(-R.sum(axis=1)).mean()
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return ba
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def sdf_Tree(r0,dr,n,p):
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x = np.ones(n)
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for i in range(n-1,-1,-1):
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R = np.arange(r0+i*dr,r0-(i+1)*dr,-2*dr)
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df = x * np.exp(-R)
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x = p*df[:-1] + (1-p)*df[1:]
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#p*x[:-1]*exp(-R[:-1]) + (1-p)*x[1:]*exp(-R[1:])
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ba = df[0]
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return ba
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r0 = 0.1
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dr = 0.01
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p = 0.6
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n = 10
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mc = sdf_MC(r0, dr, n, p)
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tree = sdf_Tree(r0, dr, n, p)
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print("MC: E[1/X] = %.6f" % mc)
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print("Tree: E[1/X] = %.6f" % tree)
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'''
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for i in range(1,11):
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df = sdf_Tree(r0,dr,i,p)
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print(-np.log(df)/i)
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'''
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sdf_Tree_vec = np.vectorize(sdf_Tree)
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i = np.arange(1,11)
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dfs = sdf_Tree_vec(r0, dr, i, p)
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R = -np.log(dfs)/i
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Rstr = ["{0:.5%}".format(r) for r in R]
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print(Rstr)
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plt.plot(i,R,'s-')
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‎new Season/portfolio_return_simulation_sol.py‎

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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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import time
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p = pd.read_excel("stockprice.xlsx")
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p.index = p.pop('Date')
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r = np.log(p).diff()
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r.dropna(inplace=True)
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print(r.describe())
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print(r.corr())
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pp = p / p.ix[0,:]
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pp.plot(figsize=(6,3))
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#weight: 투자비중
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#ret: 종목별 일간로그수익률 데이터 (pandas.DataFrame)
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#n: 시뮬레이션 회수
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#res: 함수 실행 결과 시뮬레이션 시나리오별 포트폴리오 수익률 (n개)
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def histSimulation(weight, ret, n):
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res = np.zeros(n)
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s = np.random.randint(0,len(ret),(n,20))
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for i in range(n):
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sampled = np.exp(ret.ix[s[i,:],:].sum())-1
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pr = np.dot(weight, sampled)
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res[i] = pr
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return res
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def mnSimulation(weight, ret, n):
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res = np.zeros(n)
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for i in range(n):
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temp = np.random.multivariate_normal(ret.mean(), ret.cov(), 20)
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sampled = np.exp(temp.sum(axis=0))-1
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pr = np.dot(weight, sampled)
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res[i] = pr
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return res
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def performSimulation(weight, ret, n, fun):
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res = fun(weight, ret, n)
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fig, ax = plt.subplots(1,1,figsize=(6,4))
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ax.hist(res, bins=50)
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temp = [res.mean(),res.std(),np.percentile(res,1),np.percentile(res,99)]
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xmax = res.max()
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t = "mean={0:.3%}\nstd={1:.3%}\n1% quantile={2:.3%}\n99% quantile={3:.3%}"
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ax.text(xmax+0.01, n/50, t.format(temp[0],temp[1],temp[2],temp[3]), fontsize=12)
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ax.set_xlim([res.min(), res.max()])
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ax.set_title(fun.__name__)
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fig.show()
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numOfStocks = 5
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w = np.ones(numOfStocks) / numOfStocks
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n = 10000
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t0 = time.time()
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performSimulation(w,r,n,histSimulation)
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performSimulation(w,r,n,mnSimulation)
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print("computation time = %0.3f" % (time.time()-t0))
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‎new Season/practice.bat‎

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python "c:\FEProgramming1\new Season\practice1_sol.py"

‎new Season/practice1_sol.py‎

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Prob = s/N
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pi = Prob * 4
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print(pi)
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'''
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#%%
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import random
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import numpy as np
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N = 100000
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count = 0
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r = np.random.random((N,2))
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#x: min value
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#y: max value
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x1 = r.min(axis = 1)
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x2 = 1 - r.max(axis=1)
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x3 = r.max(axis=1) - r.min(axis=1)
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x = np.vstack((x1,x2,x3))
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mx = x.max(axis=0)
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i = mx < 0.5
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print("{0:.4%}".format(i.sum()/N))
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p = r[i,0:2]
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import matplotlib.pyplot as plt
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plt.scatter(p[:,0],p[:,1],marker='.')
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plt.show()
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'''
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for i in range(N):
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x = random.random()
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y = random.random()
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prob = count/N
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print(prob)
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'''
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#%%
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import math
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number = "02-769-3937"
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calcLength(number)
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'''
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for i in range(len(n)-1):
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s = n[i]
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e = n[i+1]

‎new Season/practice3.docx‎

104 KB
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‎new Season/practice4.docx‎

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‎new Season/stockprice.xlsx‎

93.5 KB
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