Chap3 finished (Paused, not totally finished)
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.vscode
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**.pdf
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**.pdf
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**__pycache__**
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{
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"python.pythonPath": "/home/jason/miniconda3/envs/quants/bin/python"
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"python.pythonPath": "/home/jason/miniconda3/envs/quants/bin/python",
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"cSpell.words": [
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"GOOG",
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"stdev"
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]
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}
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@ -278,3 +278,22 @@ MOM的含义：
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时间序列分析因为较为advanced,因此这里暂时略过
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## Conclusion
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* 介绍了产生交易信号的基本概念，如 **阻力线、支撑线**
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* 介绍了一些简单的技术指标
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* 介绍了一些更加高级的数学方法
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* Autoregressive (AR)
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* Moving Average (MA)
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* Differentiation (D)
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* AutoCorrelation Function (ACF)
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* Partial Autocorrelation Function (PACF)
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## Further Reading
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需要了解更多的概念:
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* stochastic processing
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* random walks
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* martingales
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* time series analysis
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# Chapter 3. Predicting the Markets with Basic Machine Learning
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## 1. 了解术语和符号
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* **Supervised Learning Problem (有监督学习) vs Unsupervised Learning Problem**:
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> 有监督学习是从标签化训练数据集中推断出函数的机器学习任务
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>
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> 无监督学习是一种机器学习的训练方式，它本质上是一个统计手段，在没有标签的数据里可以发现潜在的一些结构的一种训练方式。
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> 它主要具备3个特点：
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> * 无监督学习没有明确的目的
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> * 无监督学习不需要给数据打标签
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> * 无监督学习无法量化效果
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ML在算法交易的领域经常用的是有监督学习
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* **Regression Problem vs Classification Problem**:
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> 监督学习中，如果预测的变量是离散的，我们称其为**分类**（如决策树，支持向量机等），如果预测的变量是连续的，我们称其为**回归**。
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* **Training Model & Testing Model**
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使用已由数据来训练模型，这个过程被成为 training model, 得出的模型的 parameter 被称为 **statistical inference of these parametric learning models**
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在训练模型结束后，使用训练好的模型来预测，这个过程被称为 **testing model**, 使用的数据为 **test data**
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* **Performance Metrics (性能指标)**
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得到测试结果后，需要建立 performance metrics 来评估模型
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* 对回归问题，需要减少 predicted value 和 actual value 之间的**差 residual errors** (i.e. minimize residual errors), 差的计算方法可以是:
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* sum of residual errors
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* 或 square of residual errors $R^2$
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### 1.1 Exploring our financial dataset (处理金融数据)
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ML技术需要预处理数据集，可分为3步:
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1. 获取数据
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2. 定义我们想要预测的数据（应变量）
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3. 将数据集分为训练数据集和测试数据集
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#### Step 1: Getting the data
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此处我们仍然使用 GOOG 股价
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```
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High Low Open Close Volume Adj Close
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Date
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2004-08-19 51.835709 47.800831 49.813286 49.982655 44871300.0 49.982655
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2004-08-20 54.336334 50.062355 50.316402 53.952770 22942800.0 53.952770
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2004-08-23 56.528118 54.321388 55.168217 54.495735 18342800.0 54.495735
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2004-08-24 55.591629 51.591621 55.412300 52.239193 15319700.0 52.239193
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2004-08-25 53.798351 51.746044 52.284027 52.802086 9232100.0 52.802086
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... ... ... ... ... ... ...
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2017-12-22 1064.199951 1059.439941 1061.109985 1060.119995 755100.0 1060.119995
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2017-12-26 1060.119995 1050.199951 1058.069946 1056.739990 760600.0 1056.739990
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2017-12-27 1058.369995 1048.050049 1057.390015 1049.369995 1271900.0 1049.369995
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2017-12-28 1054.750000 1044.770020 1051.599976 1048.140015 837100.0 1048.140015
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2017-12-29 1049.699951 1044.900024 1046.719971 1046.400024 887500.0 1046.400024
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[3366 rows x 6 columns]
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```
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#### Step 2: Creating objectives (trading conditions that we want to predict)
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获取数据以后，需要创造出需要预测的应变量(response variable); 对GOOG股价，我们会预测
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1. 若为了预测未来价格，就要预测价格方向（上升、下降、不便），和程度(+10, +3.4, -4); 可用回归
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2. 若为了只预测价格上升下降；可用分类
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为了处理这两种情况，需要两种trading condition generation 方法:
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* `create_classification_trading_condition(df)`: 如果明天的收盘价高于今天 classification response variable为$+1$;反之为$-1$;
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* `create_regression_trading_condition(df)`: 如果明天的收盘价高于今天, classification response variable 为 positive value; 反之则为负值;
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Classification trading condition created:
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```
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( Open-Close High-Low
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Date
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2004-08-19 -0.169369 4.034878
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2004-08-20 -3.636368 4.273979
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2004-08-23 0.672482 2.206730
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2004-08-24 3.173107 4.000008
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2004-08-25 -0.518059 2.052307
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... ... ...
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2017-12-22 0.989990 4.760010
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2017-12-26 1.329956 9.920044
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2017-12-27 8.020020 10.319946
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2017-12-28 3.459961 9.979980
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2017-12-29 0.319946 4.799927
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[3366 rows x 2 columns], array([ 1, 1, -1, ..., -1, -1, -1]))
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```
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Regression trading condition created:
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```
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( Open-Close High-Low
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Date
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2004-08-19 -0.169369 4.034878
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2004-08-20 -3.636368 4.273979
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2004-08-23 0.672482 2.206730
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2004-08-24 3.173107 4.000008
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2004-08-25 -0.518059 2.052307
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... ... ...
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2017-12-22 0.989990 4.760010
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2017-12-26 1.329956 9.920044
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2017-12-27 8.020020 10.319946
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2017-12-28 3.459961 9.979980
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2017-12-29 0.319946 4.799927
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[3366 rows x 2 columns], Date
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2004-08-19 3.970116
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2004-08-20 0.542965
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2004-08-23 -2.256542
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2004-08-24 0.562893
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2004-08-25 0.951431
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...
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2017-12-22 -3.380005
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2017-12-26 -7.369995
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2017-12-27 -1.229980
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2017-12-28 -1.739990
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2017-12-29 NaN
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```
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#### Step 3: 将数据集分成训练集和测试集
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一般来说，我们会把已有数据分为几分，然后在一份数据集上进行建模，另一份进行测试
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## 2. Creating predictive models using linear regression methods 用线性回归来建立预测模型
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Regression: Linear & Non-linear
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* 线性回归:
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* Ordinary Least Squares (OLS)
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* Lasso
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* Ridge
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* Elastic Net
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* 非线性回归:
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* Decision Tree
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### 2.1 Ordinary Linear Squares (普通最小二乘法)
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OLS:
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* 假设有:
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* $y$ 为 $m\times 1$的 target variable
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* feature values 有 $m \times$行， 每行 $1\times n$
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* OLS 希望能够发现
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OLS 要解决的数学问题$min||X\bullet W - y||^2_2$， 即找出$X\bullet W=y$的最近似方程;
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* $X$ 是 matrix of feature values
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* $W$ 是 $n\times 1$ matrix/vector
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$m=4$ & $n=2$
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$$
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min
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\begin{Vmatrix}
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\begin{bmatrix}
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x00 & x01\\
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x10 & x11\\
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x20 & x21\\
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x30 & x31
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\end{bmatrix}
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\bullet
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\begin{bmatrix}
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w_0\\
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w_1
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\end{bmatrix}
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-
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\begin{bmatrix}
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y_0\\
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y_1\\
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y_2\\
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y_3
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\end{bmatrix}
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\end{Vmatrix}^2_2
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$$
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### 2.2 Regularization and shrinkage
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### 2.3 Decision tree regression
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## 3. Creating predictive models using linear classification methods
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### 3.1 K-nearest Neighbors
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### 3.2 Support Vector Machine
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### 3.3 Logistic Regression
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from itertools import tee
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import pandas as pd
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import matplotlib.pyplot as plt
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from pandas import isna
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from prepare_financial_data import *
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dir_path = os.path.dirname(os.path.realpath(__file__))
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goog_data = load_financial_data(
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start_date = '2001-01-01',
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end_date = '2018-01-01',
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output_file = 'goog_data_large.pkl'
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)
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X, Y = create_regression_trading_condition(goog_data)
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X = X[:-1]
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Y = Y[:-1]
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goog_data = goog_data.assign(Target=pd.Series(Y, index=goog_data.index))
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print(goog_data)
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goog_data = goog_data[:-1]
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print(goog_data)
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pd.plotting.scatter_matrix(goog_data[['Open-Close', 'High-Low', 'Target']], diagonal='kde')
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plt.savefig(dir_path + "/scatter_matrix.png")
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""" Split 80% of available data into training feature value and target variable; and remaining 20% of dataset into out-sample testing feature value """
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X_train, X_test, Y_train, Y_test = create_train_split_group(X,Y,split_ratio=0.8)
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from sklearn import linear_model
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ols = linear_model.LinearRegression()
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ols.fit(X_train, Y_train)
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print('Coefficients: ', ols.coef_)
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""" Performance Matrices """
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from sklearn.metrics import mean_squared_error, r2_score
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# The mean squared error
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print("Mean squared error: {}".format(mean_squared_error(Y_train, ols.predict(X_train))))
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print(Y_test)
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print(X_test)
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# Explained variance score: 1 is perfect prediction
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# print("Variance score: " + (r2_score(Y_test, ols.predict(X_test))))
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goog_data['Predicted_Signal'] = ols.predict(X)
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goog_data['GOOG_Returns'] = np.log(goog_data['Close']/goog_data['Close'].shift(1))
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def calculate_return(df, split)
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import pandas as pd
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import os as os
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from pandas_datareader import data
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from sklearn.model_selection import train_test_split
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import numpy as np
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dir_path = os.path.dirname(os.path.realpath(__file__))
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def load_financial_data(start_date, end_date, output_file):
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try:
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df = pd.read_pickle(output_file)
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print("File data found...reading GOOG data")
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except FileNotFoundError:
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print("File not found...downloading the GOOG data")
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df = data.DataReader('GOOG', 'yahoo', start_date, end_date)
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df.to_pickle(output_file)
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return df
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def create_classification_trading_condition(df):
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df['Open-Close'] = df.Open - df.Close
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df['High-Low'] = df.High - df.Low
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df = df.dropna()
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X = df[['Open-Close', 'High-Low']]
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Y = np.where(df['Close'].shift(-1) > df['Close'], 1, -1)
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return (X,Y)
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def create_regression_trading_condition(df):
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df['Open-Close'] = df.Open - df.Close
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df['High-Low'] = df.High - df.Low
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df = df.dropna()
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print(df)
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X = df[['Open-Close', 'High-Low']]
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Y = df['Close'].shift(-1) - df['Close']
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return(X,Y)
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def create_train_split_group(X, Y, split_ratio=0.8):
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# Split dataset into two groups
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return train_test_split(X, Y, shuffle=False, train_size=split_ratio)
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