基于逻辑回归模型，我们可以去预算市场未来的走势。
示例代码大约有94%的正确率。
要说的都在代码注释里面。
cat("\014")
# 载入示例股票
library(quantmod)
getSymbols("^DJI", src = "yahoo")
dji <- DJI[, "DJI.Close"]
# 生成技术指标
avg10 <- rollapply(dji, 10, mean)
avg20 <- rollapply(dji, 20, mean)
std10 <- rollapply(dji, 10, sd)
std20 <- rollapply(dji, 20, sd)
rsi5 <- RSI(dji, 5, "SMA")
rsi14 <- RSI(dji, 14, "SMA")
macd12269 <- MACD(dji, 12, 26, 9, "SMA")
macd7205 <- MACD(dji, 7, 20, 5, "SMA")
bbands <- BBands(dji, 20, "SMA", 2)
# 生成市场方向,收盘价与之后20天价格比较
direction <- NULL
direction[dji > Lag(dji, 20)] <- 1
direction[dji < Lag(dji, 20)] <- 0
# 合并结果
dji <-
cbind(dji,
avg10,
avg20,
std10,
std20,
rsi5,
rsi14,
macd12269,
macd7205,
bbands,
direction)
dm <- dim(dji)
dm
colnames(dji)[dm[2]] <- "Direction"
colnames(dji)[dm[2]]
# 样本内is和样本外os
issd <- "2010-01-01"
ised <- "2014-12-31"
ossd <- "2015-01-01"
osed <- "2015-12-31"
isrow <- which(index(dji) >= issd & index(dji) <= ised)
osrow <- which(index(dji) >= ossd & index(dji) <= osed)
isdji <- dji[isrow,]
osdji <- dji[osrow,]
# 数据标准化转化
isme <- apply(isdji, 2, mean, na.rm = TRUE)
isstd <- apply(isdji, 2, sd, na.rm = TRUE)
isidn <- matrix(1, dim(isdji)[1], dim(isdji)[2])
norm_isdji <- (isdji - t(isme * t(isidn))) / t(isstd * t(isidn))
dm <- dim(isdji)
norm_isdji[, dm[2]] <- direction[isrow]
# 建模
formula <- as.formula("Direction ~ .")
model <- glm(formula, family = "binomial", data = norm_isdji)
summary(model)
pred <- predict(model, norm_isdji)
prob <- 1 / (1 + exp(-pred))
# 拟合效果和概率值
# par(mfrow = c(2, 1))
# 还是这个问题：Error in plot.new() : figure margins too large
plot(pred, type = "l")
plot(prob, type = "l")
pred_direction <- NULL
pred_direction[prob > 0.5] <- 1
pred_direction[prob <= 0.5] <- 0
# 模型预测正确率
library(caret)
ismatrix <- confusionMatrix(as.factor(pred_direction),
as.factor(norm_isdji$Direction))
ismatrix
# 样本外数据测试泛化性能
osidn <- matrix(1, dim(osdji)[1], dim(osdji)[2])
norm_osdji <- (osdji - t(isme * t(osidn))) / t(isstd * t(osidn))
norm_osdji[, dm[2]] <- direction[osrow]
ospred <- predict(model, norm_osdji)
osprob <- 1 / (1 + exp(-ospred))
ospred_direction <- NULL
ospred_direction[osprob > 0.5] <- 1
ospred_direction[osprob <= 0.5] <- 0
osmatrix <- confusionMatrix(as.factor(ospred_direction),
as.factor(norm_osdji$Direction))
osmatrix
结果
模型概况
> summary(model)
Call:
glm(formula = formula, family = "binomial", data = norm_isdji)
Deviance Residuals:
Min 1Q Median 3Q Max
-3.0080 -0.0107 0.0366 0.1533 3.1790
Coefficients: (3 not defined because of singularities)
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.658760 0.222691 7.449 9.43e-14 ***
DJI.Close 44.051359 8.409499 5.238 1.62e-07 ***
DJI.Close.1 -44.561952 17.549358 -2.539 0.0111 *
DJI.Close.2 0.577137 17.620013 0.033 0.9739
DJI.Close.3 -0.003556 0.291865 -0.012 0.9903
DJI.Close.4 -0.264309 0.312768 -0.845 0.3981
rsi 0.046117 0.339620 0.136 0.8920
rsi.1 -2.306590 0.565594 -4.078 4.54e-05 ***
macd 2.562233 1.300929 1.970 0.0489 *
signal 1.476838 0.610356 2.420 0.0155 *
macd.1 -1.032963 0.798086 -1.294 0.1956
signal.1 3.871052 1.635221 2.367 0.0179 *
dn NA NA NA NA
mavg NA NA NA NA
up NA NA NA NA
pctB 1.269642 0.521006 2.437 0.0148 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
(Dispersion parameter for binomial family taken to be 1)
Null deviance: 1579.37 on 1257 degrees of freedom
Residual deviance: 348.17 on 1245 degrees of freedom
AIC: 374.17
Number of Fisher Scoring iterations: 8模型拟合效果
概率
样本内数据正确率93.88%
> ismatrix
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 362 35
1 42 819
Accuracy : 0.9388
95% CI : (0.9241, 0.9514)
No Information Rate : 0.6789
P-Value [Acc > NIR] : <2e-16
Kappa : 0.859
Mcnemar's Test P-Value : 0.4941
Sensitivity : 0.8960
Specificity : 0.9590
Pos Pred Value : 0.9118
Neg Pred Value : 0.9512
Prevalence : 0.3211
Detection Rate : 0.2878
Detection Prevalence : 0.3156
Balanced Accuracy : 0.9275
'Positive' Class : 0 样本外数据正确率84.92%
> osmatrix
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 115 26
1 12 99
Accuracy : 0.8492
95% CI : (0.7989, 0.891)
No Information Rate : 0.504
P-Value [Acc > NIR] : < 2e-16
Kappa : 0.6981
Mcnemar's Test P-Value : 0.03496
Sensitivity : 0.9055
Specificity : 0.7920
Pos Pred Value : 0.8156
Neg Pred Value : 0.8919
Prevalence : 0.5040
Detection Rate : 0.4563
Detection Prevalence : 0.5595
Balanced Accuracy : 0.8488
'Positive' Class : 0 感谢阅读，欢迎关注和留言
量化投资与期货外汇散仙，基金保险水平也拿的出手