Problem Definition: Portfolio Management 1 符号定义 资产数量:m; 第 t 个周期的收盘价格向量: \boldsymbol{v}_{t} ,其中 v_{...A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem[J].
如果接触过优矿、ricequant这样的平台的同学,可能觉得backtrader不适合做这样的portfolio层面的回测。...1.最终的效果 和一般的portfolio层面的回测平台一样,我们希望,最后我们实现一个策略只要进行一些设置就可以了。...=portfolio_csv_name, bechmark_csv_name=benchmark_csv_name) 然后,回测结束后输出评价指标。...end = datetime.datetime.now() print "time elapse:", (end - begin) print 'Start Portfolio Value: %.2f'...% result_dict['start_cash'] print 'Final Portfolio Value: %.2f' % result_dict['final_value'] print '
# 让我们使用Pierre的平均输入假设 #***************************************************************** obj = portfolio...(c(1,3,6,12), 0), min.risk.fns = list( TRISK.AVG = target.risk.portfolio(target.r ) 最后,我们准备看一下结果...---- 本文摘选《R语言动量和马科维茨Markowitz投资组合(Portfolio)模型实现》
***** # 让我们使用Pierre的平均输入假设 #***************************************************************** obj = portfolio...(c(1,3,6,12), 0), min.risk.fns = list( TRISK.AVG = target.risk.portfolio(target.r ) 最后,我们准备看一下结果 #*
3: Add Tangency Portfolio 4: Add Risk/Return of Single Assets5: Add Equal Weights Portfolio6:...: Add Tangency Portfolio4: Add Risk/Return of Single Assets5: Add Equal Weights Portfolio6: Add...: Add Tangency Portfolio4: Add Risk/Return of Single Assets5: Add Equal Weights Portfolio6: Add...: Add Tangency Portfolio4: Add Risk/Return of Single Assets5: Add Equal Weights Portfolio6: Add...: Add Tangency Portfolio4: Add Risk/Return of Single Assets5: Add Equal Weights Portfolio6: Add
,safeadmin http://docker.hackthebox.eu:30144/portfolio.php?...python sqlmap.py -u "http://docker.hackthebox.eu:30336/portfolio.php?...id=2" --file-read=/var/www/html/portfolio.php index.php没什么实质性内容,可能是目录猜错了,菜的想哭~ 没想到portfolio.php来了个惊喜~...尝试直接写shell: http://docker.hackthebox.eu:30883/portfolio.php?...python sqlmap.py -u http://docker.hackthebox.eu:30796/portfolio.php?
public void setUp(){ portfolio = new Portfolio(); stockService = mock(StockService.class);...Mockito实现了 @Mock将外部依赖StockService 进行了mock @InjectMocks通过调用Portfolio类的无参构造方法完成了portfolio的实例化,并通过Portfolio...进行一下重构,新增加了一个带参的构造方法Portfolio(StockService stockService)。...Portfolio() { System.out.println("Constructor 0 called"); } // public Portfolio(StockService stockService...类的无参构造方法为portfolio进行了实例化,并且在这个过程顺利地将StockService进行了mock,注入到了portfolio中的stockService变量。
/rdr keys example.rdb portfolio:stock_follower_count:ZH314136 portfolio:stock_follower_count:ZH654106...portfolio:stock_follower:ZH617824 portfolio:stock_follower_count:ZH001019 portfolio:stock_follower_count...:ZH346349 portfolio:stock_follower_count:ZH951803 portfolio:stock_follower:ZH924804 portfolio:stock_follower_count
-- Portfolio --> <!
=np.log(stock_portfolio)-np.log(stock_portfolio.shift(1)) stock_portfolio.head() 显然,第一行是空值,因此我们将其去掉:...) stock_cov=stock_portfolio.cov()*len(stock_portfolio.index) r_portfolio=np.dot(stock_mean,W) #得到投资组合的期望收益...var_portfolio=np.dot(W,stock_cov) var_portfolio=np.dot(var_portfolio,W.T) #得到投资组合的方差 sharp_ratio=r_portfolio...) stock_cov=stock_portfolio.cov()*len(stock_portfolio.index) r_portfolio=np.dot(stock_mean,W)...()*len(stock_portfolio.index) stock_cov=stock_portfolio.cov()*len(stock_portfolio.index) r_portfolio
package com.github.stock; import java.util.List; public class Portfolio { private StockService...package com.github.stock; //import省略 public class PortfolioTest { Portfolio portfolio; StockService...stockService; @BeforeEach public void setUp(){ //Create a portfolio object which is...to be tested portfolio = new Portfolio(); //Create the mock object of stock service...portfolio.setStockService(stockService); } @Test public void testMarketValue(){
values for each scenario portfolio_values = (data["Close"].iloc[-1] * (1 + simulated_returns)).cumprod...() # Convert portfolio_values into a DataFrame portfolio_values = pd.DataFrame(portfolio_values)...# Calculate portfolio returns for each scenario portfolio_returns = portfolio_values.pct_change()...# Calculate VaR if len(portfolio_returns.iloc[-1].dropna()) > 0: var = -np.percentile(portfolio_returns.iloc...returns plt.hist(portfolio_returns.iloc[-1].dropna(), bins=50, density=True, alpha=0.7) # Plot VaR
double marketValue); } 来看一下测试用例 @Test public void testPortfolioVerifySave(){ //add stocks to the portfolio...portfolio.setStocks(stocks); //mock the behavior of stock service to return the value of various...thenReturn(500.00); when(stockService.getPrice(amazonStock)).thenReturn(1000.00); assertThat(portfolio.getMarketValue...如一下这个案例 @Test public void testPortfolioVerifySaveNotCalled(){ //add stocks to the portfolio portfolio.setStocks...teslaStock)).thenReturn(0.0); when(stockService.getPrice(amazonStock)).thenReturn(0.0); assertThat(portfolio.getMarketValue
字典 get 和 setdefault 方法 ##不推荐 navs = {} for (portfolio, equity, position) in data: if portfolio not...in navs: navs[portfolio] = 0 navs[portfolio] += position * prices[equity] ##推荐 navs...= {} for (portfolio, equity, position) in data: # 使用 get 方法 navs[portfolio] = navs.get(portfolio..., 0) + position * prices[equity] # 或者使用 setdefault 方法 navs.setdefault(portfolio, 0) navs[...portfolio] += position * prices[equity] 8.
——哈里·马克维茨 “现代投资组合理论”(MPT)是1952年哈里.马克维茨在金融期刊上发表的名为“Portfolio Selection”的文章上提出的投资理论。 ?...“portfolio_annualised_performance”函数可以计算年收益率和波动性(一年以252个交易日计算)。”..., portfolio_return = portfolio_annualised_performance(weights, mean_returns, cov_matrix) results...[0,i] = portfolio_std_dev results[1,i] = portfolio_return results[2,i] = (portfolio_return...def portfolio_volatility(weights, mean_returns, cov_matrix): return portfolio_annualised_performance
public void testPortfolioGetMarketValueMultiTimes(){ //Creates a list of stocks to be added to the portfolio...","amazon",100); stocks.add(teslaStock); stocks.add(amazonStock); //add stocks to the portfolio...portfolio.setStocks(stocks); //mock the behavior of stock service to return the value of various...stockService.getPrice(amazonStock)) .thenReturn(1000.00).thenReturn(0.0).thenReturn(1.0);; assertThat(portfolio.getMarketValue...()).isEqualTo(105000.0); assertThat(portfolio.getMarketValue()).isEqualTo(0.0); assertThat(portfolio.getMarketValue
计算等权重组合的收益 StockReturns['Portfolio_EW'] = stock_return.mul(portfolio_weights_ew, axis=1).sum(axis=1)...# 打印前5行数据 print(StockReturns.head()) # 绘制累积收益曲线 cumulative_returns_plot(['Portfolio', 'Portfolio_EW']...', 'Portfolio_EW', 'Portfolio_MVal']) 实际问题中,我们需要根据最终的投资累积收益来确定最优的投资组合。...# 计算投资组合的标准差 portfolio_volatility = np.sqrt(np.dot(portfolio_weights.T, np.dot(cov_mat_annual, portfolio_weights...))) print(portfolio_volatility) 四、探索股票的最优投资组合 应该选择怎样的组合权重才是最好的呢?
Interest rate factor 是影响利率曲线上各个独立利率的random variables hedger 需要理解在利率变化时如何hedge investor 需要决定当利率发生期望改变的最优投资 portfolio...manager 需要知道当利率发生期望改变是的portfolio波动率 A/L manager 需要匹配利率敏感资产和负债 59.2 定义和计算DV01,根据收益率变化计算价格变化 DV01: dollar...注意:可能为正,也可能为负 59.7 描述计算一个portfolio的effective duration和convexity的过程 duration of portfolio = ?...convexity of portfolio = ?...计算duration of portfolio时有个很大的限制是,所有bond必须perfectly correlated however,it is unlikely that yield cross
, 50, 123.45) print(','.join(str(x) for x in s)) # Data reduction across fields of a data structure portfolio...name':'AOL', 'shares': 20}, {'name':'SCOX', 'shares': 65} ] min_shares = min(s['shares'] for s in portfolio...例如在 portfolio 的例子中,可以使用下面的替代方案: # Original: Returns 20 min_shares = min(s['shares'] for s in portfolio...) # Alternative: Returns {'name': 'AOL', 'shares': 20} min_shares = min(portfolio, key=lambda s: s['shares
connect('database.db') db.cursor c.execute() db.commit() c = db.cursor() c.execute('create table portfolio..., price real)') db.commit() >>> c.executemany('insert into portfolio...stocks) db.commit() for row in db.execute('select * from portfolio...('FB', 150, 7.45) ('HPQ', 75, 33.2) min_price = 100 for row in db.execute('select * from portfolio
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