深度强化学习框架：金融投资组合管理的新视角

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本文档"Deep RL for Financial Portfolio.pdf"主要探讨了强化学习（Reinforcement Learning, RL）在金融投资组合管理中的应用。强化学习是一种人工智能领域的学习方式，其目标是通过智能体（agent）与环境的交互，不断优化策略以实现长期利益最大化。在投资组合管理中，这个过程涉及到资产配置、风险评估和收益优化等复杂决策问题。作者提出了一种深度强化学习框架（DeepPortfolioManagement），旨在解决金融投资组合管理问题，该框架不依赖于传统的金融模型。这个框架的核心组成部分包括： 1. **Ensemble of Identical Independent Evaluators (EIIE)**：这是一个评估策略集合的设计，每个独立的评估器对不同的投资策略进行并行评估，增强了学习过程的效率和多样性。 2. **Portfolio-Vector Memory (PVM)**：这是一种记忆机制，用于存储和检索过去的投资组合状态和对应的回报信息，以便智能体可以从中学习并调整策略。 3. **Online Stochastic Batch Learning (OSBL)**：这是一种在线学习算法，结合了批量学习的优点（减少噪声的影响）和在线学习的实时性，允许在实际市场环境中快速迭代优化策略。 4. **Fully Exploiting and Explicit Reward Function**：为了确保学习的导向性，该框架采用了一个全面利用且明确的奖励函数，它直接反映投资决策的成功与否，如收益率、风险水平等因素。论文具体采用了卷积神经网络（Convolutional Neural Network, CNN）和基本循环神经网络（Recurrent Neural Network）这两种深度学习技术来实现这一框架。CNN用于处理投资组合数据的特征提取，而RNN则可能用于捕捉时间序列数据中的动态模式。通过这些组件的协同工作，该研究旨在开发出一个能够自主适应市场变化，优化投资组合结构，从而提高投资回报并控制风险的智能投资系统。这种方法的潜在优势在于其灵活性和自我学习能力，使得机器可以在不断变化的金融市场环境中持续优化策略。这篇论文为金融领域的机器学习应用提供了一个新颖且实用的解决方案。

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Jiang, Xu and Liang

time

t − 1

t + 1

Perio d t Perio d t + 1

t−1

′

t+1

′

t+1

′

buy

sell

t+1

· · · · · ·

Figure 1: Illustration of the eﬀect of transaction remainder factor µ

. The market movement

during Period t, represented by the price-relative vector y

, drives the portfolio

value and portfolio weights from p

t−1

and w

t−1

to p

′

and w

′

. The asset selling and

purchasing action at time t redistributes the fund into w

. As a s ide-eﬀect, these

transactions shrink the portfolio to p

by a factor of µ

. The rate of return for

Perio d t is calculated using portfolio values at the beginn ing of the two consecutive

periods in Equation (9).

now on. µ

is to b e determined below. Denoting p

t−1

as the portfolio value at the beginning

of Period t and p

′

at th e end,

= µ

′

. (8)

The rate of return (3) and logarithmic rate of return (4) are now

t−1

− 1 =

′

t−1

− 1 = µ

· w

t−1

− 1, (9)

= ln

t−1

= ln (µ

· w

t−1

) , (10)

and the ﬁnal portfolio value in Equation (6) becomes

= p

exp

t=1

= p

t=1

· w

t−1

. (11)

Diﬀerent from Equation (4) and (2) where transaction cost is not considered, in Equa-

tion (10) and (11), p

′

6= p

and the diﬀerence between the two values is where the trans-

action remainder factor comes into play. Figure 1 demonstrates the relationship among

portfolio vectors and values and their dynamic relationship on a time axis.

The remaining problem is to determine this tr an saction remainder factor µ

. During

the portfolio reallocation from w

′

to w

, some or all amount of asset i need to be sold, if

′

t,i

> p

t,i

or w

′

t,i

> µ

t,i

. The total amount of cash obtained by all selling is

(1 − c

′

i=1

′

t,i

− µ

t,i

)

(12)

where 0 6 c

< 1 is the commission rate for selling, and (v)

= ReLu(v) is the element-wise

rectiﬁed linear function, (x)

= x if x > 0, (x)

= 0 otherwise. This money and the original

cash reserve p

′

t,0

taken away the new reserve µ

′

t,0

will be used to buy new assets,

(1 − c

)

′

t,0

+ (1 − c

)

i=1

′

t,i

− µ

t,i

)

− µ

t,0

i=1

(µ

t,i

− w

′

t,i

)

, (13)

Deep Portfolio Management

where 0 6 c

< 1 is the commission rate for purchasing, and p

′

has been canceled out on

both sides. Using identity (a − b)

− (b − a)

= a − b, and the fact that w

′

t,0

i=1

′

t,i

1 = w

t,0

i=1

t,i

, Equation (13) is simpliﬁed to

1 − c

t,0

1 − c

′

t,0

− (c

+ c

− c

)

i=1

′

t,i

− µ

t,i

)

. (14)

The presence of µ

inside a linear rectiﬁer means µ

is not solvable analytically, but it can

only be solved iteratively.

Theorem 1 Denoting

f(µ) :=

1 − c

t,0

1 − c

′

t,0

− (c

+ c

− c

)

i=1

′

t,i

− µw

t,i

)

the sequence

˜µ

(k)

, deﬁned as

˜µ

(k)



˜µ

(0)

= µ

⊙

and ˜µ

(k)

= f



˜µ

(k−1)



, k ∈ N

(15)

converges to µ

, the solution to Equation (14), for any µ

⊙

∈ [0, 1].

While this convergence is not stated in Ormos and Urb´an (2013), its proof will be given

in App endix A. This theorem provides a way to approximate the transaction remainder

factor µ

to an arbitrary accuracy. The speed on the convergence depends on the error

of the initial guest µ

⊙

. The smaller |µ

− µ

⊙

| is, the quicker Sequence (15) converges to

. When c

= c

= c, there is a practice (Moody et al., 1998) to approximate µ

with

i=1

′

t,i

− w

t,i

|. Therefore, in this work, µ

⊙

will use this as the ﬁrst value for the sequ ence,

that

⊙

= c

i=1



′

t,i

− w

t,i



. (16)

In the training of the neural networks, ˜µ

(k)

with a ﬁxed k in (15) is used. In th e back-

test experiments, a tolerant error δ dynamically determines k, that is the ﬁr st k, such that



˜µ

(k)

− ˜µ

(k−1)



< δ , is used for ˜µ

(k)

to approximate µ

. In general, µ

and its approximations

are functions of portfolio vectors of two recent periods and the price relative vector,

= µ

t−1

, w

, y

). (17)

Throughout this work, a single constant commission rate for both selling and purchasing

for all non-cash assets is used, c

= c

= 0.25%, the maximum rate at Poloniex.

The purpose of the algorithmic agent is to generate a time-sequence of portfolio vec-

tors {w

, w

, · · · , w

, · · · } in order to maximize the accumulative capital in (11), taking

transaction cost into account.

剩余30页未读，继续阅读

Deep learning deep portfolios

We explore the use of deep learning hierarchical models for problems in financial prediction and classification. Financial prediction problems – such as those presented in designing and pricing securities, constructing portfolios, and risk management – often involve large data sets with complex data interactions that currently are difficult or impossible to specify in a full economic model. Applying deep learning methods to these problems can produce more useful results than standard methods in finance. In particular, deep learning can detect and exploit interactions in the data that are, at least currently, invisible to any existing financial economic theory.

Deep-Portfolio-Management-Reinforcement-Learning:该存储库介绍了我们在哥伦比亚大学IEOR 8100 RL课程的背景下实现的项目中的工作

针对金融组合管理问题的深度强化学习框架该存储库介绍了在哥伦比亚大学的背景下实现的项目中我们的工作。这个深度政策网络强化学习项目是我们对原始论文实施和进一步研究。目标：问题是自动化投资组合管理的一个问题：给定一组股票，如何在一定数量的时间步长结束时最佳地通过时间分配资金以最大化回报。通过这种方式，我们的目标是建立一个自动代理，以最佳地在不同股票之间分配其投资权重。数据：姜等。使用Poloniex交易所的13种加密货币。他们考虑了每分钟的开盘价，最高价，最低价，收盘价（OHLC）。它们使投资组合每30分钟重新平衡一次。他们重新处理数据并基于最近的50个时间步创建张量。我们使用每日数据和每日重新平衡的日内数据框架将实验扩展到股票市场。该项目分为三个部分：数据预处理环境设置深度政策网络设计代理商的培训和测试这些文件是： data_pipe.ipynb

universal portfolio cover .pdf

Universal Portfolio Cover .pdf是指通用的投资组合封面文件。投资组合封面文件是对一个或多个投资组合进行概述和展示的文件。它通常包含投资组合的基本信息，如投资目标、投资策略、持仓、风险管理等。使用...

我创建了一个表格，如何增加第一列且为单选框：

策略类型策略构成:期权策略构成:ETF 最大利润最大亏损交易量

${portfolio.StrategyName}"/> ${portfolio.StrategyName}"> ${portfolio.options}"> ${portfolio.stocks}"> ${portfolio.openingPrice}"> ${portfolio.maxProfit}"> ${portfolio.maxLoss}"> ${portfolio....

import jqdata # 初始化聚宽账号密码 def initialize(context): # 设置回测日期区间 set_benchmark('000300.XSHG') set_option('use_real_price', True) # 设置买入的股票数量上限 g.max_stock_count = 5 def handle_data(context, data): # 获取当前日期 current_date = context.current_dt.date() # 获取股票池中的股票列表 stocks = get_index_stocks('000852.XSHG') # 按照股票池中的股票进行遍历 for stock in stocks: # 判断股票是否满足买入条件 if check_buy_condition(stock, current_date, context): buy_stock(stock, context) # 判断持有的股票是否满足卖出条件 if check_sell_condition(stock, current_date, context): sell_stock(stock, context) def check_buy_condition(stock, current_date, context): # 判断股票是否连续下跌三天 prices = attribute_history(stock, 3, '1d', ['close']) if len(prices) == 3 and prices['close'][-1] < prices['close'][-2] < prices['close'][-3]: return True else: return False def buy_stock(stock, context): # 判断当前持仓的股票数量是否已达上限 if len(context.portfolio.positions) >= g.max_stock_count: return # 买入股票 order_value(stock, context.portfolio.cash / g.max_stock_count) def check_sell_condition(stock, current_date, context): # 获取持有股票的买入日期 buy_date = context.portfolio.positions[stock].init_time.date() # 判断是否满足卖出条件 if current_date - buy_date >= 3: # 判断是否亏损超过5% if (context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost <= -0.05: return True return False def sell_stock(stock, context): # 卖出股票 order_target(stock, 0)当中buy_date = context.portfolio.positions[stock].init_time.date()报错'NoneType' object has no attribute 'date'

这个错误是因为在`check_sell_condition`函数中，`context.portfolio.positions[stock].init_time`返回了`None`，没有日期属性。这可能是因为你在买入股票之前没有设置买入日期。你可以在买入股票时设置买入日期，...

分析封装。 private final String name; private double liquidity; private Set<MarketProperty> portfolio; //constructors //Creating an empty portfolio of assets and zero liquidity. public PropertyManagementCompany(String name, double liquidity) { this.name = checkName(name); this.liquidity = liquidity; this.portfolio = createEmptyPortfolio(); } private Set<MarketProperty> createEmptyPortfolio() { return new TreeSet<>(Comparator.comparingDouble(MarketProperty::getCurrentValuation).reversed()); } //creating a portfolio and liquidity with parameters such as company name, liquidity, and portfolio list public PropertyManagementCompany(String name, double liquidity, List<MarketProperty> portfolio) { this.name = checkName(name); this.liquidity = liquidity; this.portfolio = createEmptyPortfolio(); this.portfolio.addAll(portfolio); } //validators private String checkName(String name) { //The aim of this method is to ensure the type of category. if (name.isEmpty() ) { throw new IllegalArgumentException("The company name can't be empty!"); } else { return name; } } //Purchase a real estate asset with the purchase price. public void buyProperty(MarketProperty property, double price) { if (liquidity >= price) { if (portfolio.contains(property)) { throw new IllegalArgumentException("The property has been held."); } else { //MarketProperty marketProperty = new MarketProperty(property.getID(), property.getCategory(), property.getSize(), property.getInitialPrice()); portfolio.add(property); liquidity -= price; } } else { throw new IllegalArgumentException("Insufficient liquidity to purchase the property. "); } } //Sell a real estate asset with the current valuation. public void sellProperty(MarketProperty property) { if (portfolio.contains(property)) { liquidity += property.getCurrentValuation(); portfolio.remove(property); } else { throw new IllegalArgumentException("Property not found in portfoliio!"); } } //getters public String getName() { return name; } public double getLiquidity() { return liquidity; } public List<MarketProperty> g

The class also has getters for the name, liquidity, and portfolio. The "checkName" method is a private helper method that checks if the company name is empty and throws an exception if it is. Overall...

现在你已知道如何读取文件，下面编写一个程序来执行简单的计算。“portfolio.csv”中的列对应于股票名称，持股数量（share），以及股票价格。请编写程序“pcost.py”，用于打开“portfolio.csv”文件，读取所有行并计算购买投资组合中所有股票的成本。提示：要将字符串转换为整数，请使用“int(s)”；要将字符串转为浮点数，可使用“float(s)”

with open('portfolio.csv', 'r') as f: rows = csv.reader(f) headers = next(rows) for row in rows: shares = int(row[1]) price = float(row[2]) total_cost += shares * price print('Total cost:', ...

def initialize(context): # 设置回测日期区间 set_benchmark('000300.XSHG') set_option('use_real_price', True) # 设置买入的股票数量上限 g.max_stock_count = 5 def handle_data(context, data): # 获取当前日期 current_date = context.current_dt.date() # 获取股票池中的股票列表 stocks = get_index_stocks('000852.XSHG') # 按照股票池中的股票进行遍历 for stock in stocks: # 判断股票是否满足买入条件 if check_buy_condition(stock, current_date, context): buy_stock(stock, context) # 判断持有的股票是否满足卖出条件 if check_sell_condition(stock, current_date, context): sell_stock(stock, context) def check_buy_condition(stock, current_date, context): # 判断股票是否连续下跌三天 prices = attribute_history(stock, 3, '1d', ['close']) if len(prices) == 3 and prices['close'][-1] < prices['close'][-2] < prices['close'][-3]: return True else: return False def buy_stock(stock, context): # 判断当前持仓的股票数量是否已达上限 if len(context.portfolio.positions) >= g.max_stock_count: return buy_date = context.current_dt.date() # 买入股票 order_value(stock, context.portfolio.cash / g.max_stock_count) def check_sell_condition(stock, current_date, context): # 获取持有股票的买入日期 buy_date = context.current_dt.date() time_diff = current_date - buy_date threshold = timedelta(days=3) # 判断是否满足卖出条件 if time_diff >= threshold or ((context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost <= -0.05): # 判断是否亏损超过5% return order_target(stock, 0) 报错 type object 'UserObject' has no attribute 'getattr'

if time_diff >= threshold or ((context.portfolio.positions[stock].last_price - context.portfolio.positions[stock].avg_cost) / context.portfolio.positions[stock].avg_cost ): # 判断是否亏损超过5% ...

ggplot(tsne_result,aes(tsn1,tsn2,color=optdigits$Portfolio.Exits))+geom_point()报错Error in ggplot(tsne_result, aes(tsn1, tsn2, color = optdigits$Portfolio.Exits)) : object 'tsne_result' not found

这个错误表示 R 无法找到名为 "tsne_result" 的对象。可能是因为你没有加载包含 "tsne_result" 数据框的 ...如果你需要使用 "optdigits$Portfolio.Exits" 列来指定颜色变量，请确保该列在 "tsne_result" 数据框中存在。

详细举例This code represents a class named "PropertyManagementCompany" which manages a portfolio of real estate properties. The class has a name, liquidity and a set of properties that it manages. The liquidity represents the amount of money the company has available to purchase new properties or sell existing ones. The class has two constructors, one that creates an empty portfolio and sets the liquidity to zero, and another that takes a list of properties and adds them to the portfolio. Both constructors also require the company name and liquidity as parameters. The class has two methods to buy and sell real estate properties. The "buyProperty" method takes a property and a purchase price as parameters and adds the property to the portfolio if the company has enough liquidity to buy it. The "sellProperty" method takes a property and removes it from the portfolio while adding the property's current valuation to the company's liquidity. The class also has getters for the name, liquidity, and portfolio. The "checkName" method is a private helper method that checks if the company name is empty and throws an exception if it is. Overall, this code uses encapsulation to hide the implementation details of the PropertyManagementCompany class. The class has private fields that can only be accessed through its public methods. This approach makes the code more modular and easier to maintain.

self.portfolio.append(property) self.liquidity -= purchasePrice def sellProperty(self, property): if property in self.portfolio: self.portfolio.remove(property) self.liquidity += property....

tsne_result<-as.data.frame(tsne$Y) > colnames(tsne_result)<-c('tsn1','tsn2') > ggplot(tsne_result,aes(tsn1,tsn2,color=cols)) > ggplot(tsne_result,aes(tsn1,tsn2))+geom_point() > library(ggplot2) > View(optdigits) > ggplot(tsne_result,aes(tsn1,tsn2,color=optdigits$Portfolio.Exits))+geom_point() > tsne$Y > plot(tsne$Y

其中，tsne_result 存储了降维后的结果，optdigits 是一个数据集，cols 和 Portfolio.Exits 是该数据集中的两个变量。具体代码解释如下： - 第一行将 t-SNE 结果转换为数据框格式； - 第二行为新的数据框添加列名；...

hhaa017.pdf

1. Aggregate stock returns can be predicted using a factor model. 2. The factor model allows for time-varying ...5. The authors propose a method for constructing the optimal factor timing portfolio.

尝试从一个字典列表创建一个 Stock 实例列表，然后计算总成本： >>> import fileparse >>> with open('Data/portfolio.csv') as lines: ... portdicts = fileparse.parse_csv(lines, select= ['name','shares','price'], types=[str,int,float]) ... >>> portfolio = [ stock.Stock(d['name'], d['shares'], d['price']) for d in portdicts] >>> portfolio [<stock.Stock object at 0x10c9e2128>, <stock.Stock object at 0x10c9e2048>, <stock.Stock object at 0x10c9e2080>, <stock.Stock object at 0x10c9e25f8>, <stock.Stock object at 0x10c9e2630>, <stock.Stock object at 0x10ca6f748>, <stock.Stock object at 0x10ca6f7b8>] >>> sum([s.cost() for s in portfolio]) 44671.15

这段代码是从一个 csv 文件中读取数据，然后使用 fileparse 模块解析数据，得到一个字典列表 portdicts。...同时，这里的数据文件名为 portfolio.csv，需要保证该文件存在，并且数据格式符合 parse_csv() 函数的要求。

matlab中portalloc的使用错误Unable to compute indifference curve tangency portfolio.

当出现"Unable to compute indifference curve tangency portfolio"的错误提示时，通常是由于投资组合中的某些资产权重没有被正确设置，导致无法计算出最优的资产权重。为了解决这个问题，您可以检查以下几个方面...

import csv def read_portfolio(filename): portfolio=[] with open(filename,'rt') as f: rows=csv.reader(f) headers=next(rows) for row in rows: holding=(row[0], int(row[1]), float(row[2])) portfolio.append(holding) return portfolio #以交互方式试验该函数： portfolio = read_portfolio('D:\data\portfolio.csv')自行设计简单的函数实现（具体功能不限），并完成以下实验：（1）默认参数的使用；（2）函数的返回值（包括无返回值、一个及多个返回值）；（3）变量的作用域；（4）在函数内部使用全局变量；（4）在函数内部修改全局变量的值；（5）以可变数据类型（例如列表和字典）作为参数，在函数内部对其进行修改，并检验函数调用前后对应变量的值的变化情况；（6）参考“3.2 有关函数的更多信息.pdf”中的“重新赋值与修改（Reassignment vs Modifying）”部分内容，设计相关实验以检验在函数体内部修改值和重新分配变量名称之间的区别。

1. 默认参数的使用： ```python def greet(name, greeting='Hello'): print(f'{greeting}, {name}!') # 调用函数 greet('Alice') # 输出：Hello, Alice! greet('Bob', 'Hi') # 输出：Hi, Bob!...

请用python写一段用brinson模型做基金业绩归因的代码

stock_excess_returns = ((portfolio.loc['current'] - portfolio.loc['beginning']) / portfolio.loc['beginning']).values - benchmark_return # 计算组合中股票选择和配置的总体贡献 stock_selection_...

用tushare获取数据写一个Python版本的中证1000指数期权的布林线交易策略

pnl = (price - portfolio.loc[date, 'price']) * qty else: pnl = np.nan portfolio.loc[date, 'cash'] = cash portfolio.loc[date, 'value'] = value portfolio.loc[date, 'pnl'] = pnl portfolio.loc[date...

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深度强化学习框架：金融投资组合管理的新视角

Deep learning deep portfolios

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universal portfolio cover .pdf

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我创建了一个表格，如何增加第一列且为单选框：

策略类型策略构成:期权策略构成:ETF 最大利润最大亏损交易量

用tushare获取数据写一个Python版本的中证1000指数期权的布林线交易策略