Daniel dalamar66

## keras_prediction.py
import pandas as pd
from random import random

flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000
pdata = pd.DataFrame({"a":flow, "b":flow})
pdata.b = pdata.b.shift(9)
data = pdata.iloc[10:] * random()  # some noise

import numpy as np

## keras.py
""" From:  http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
from keras.models import Sequential
from keras.layers.core import TimeDistributedDense, Activation, Dropout
from keras.layers.recurrent import GRU
import numpy as np

def _load_data(data, steps = 40):
    docX, docY = [], []
    for i in range(0, data.shape[0]/steps-1):
        docX.append(data[i*steps:(i+1)*steps,:])

## svm_rsi_trend.R
# Some code to asses an SVM with a two-dimensional feature space:
# trend (price - simple moving average) and relative strength index.
# The code is an adaptation of the code found in the following linkedin post:
# "Trading the RSI using a Support Vector Machine"
# "https://www.linkedin.com/pulse/article/20141103165037-172934333-trading-the-rsi-using-a-support-vector-machine"

# Settings
sma.window = 50 # Number of observations in simple moving average.
rsi.window = 3  # Number of observation in relative strength index (RSI)

## Cumulative Utility of Household Debt.R
library(quantmod)
getSymbols(c("GDP", "CMDEBT"), src="FRED")
plot(cumsum(na.omit(diff(GDP) - diff(CMDEBT))))

## currency.png

      
              6 files
            
          
              0 forks
            
          
                0 comments
              
            
              0 stars
            
          
                dalamar66
                / currency.png
            
            
              Created
              January 25, 2017 16:12
                — forked from mrbcuda/currency.png
            
              
                A mashup of financial turbulence and regime switching examples having missing bits into a standalone example without missing bits. Uses sources from Quantivity and Systematic Investor blogs as well as the CRAN RHmm and TTR packages. Uses quantmod and FRED as a data source. The turbulence calculation clearly is not the same as referenced original…
              
          
## DecisionTree.py
import os
import io
import subprocess
import pandas as pd
import numpy as np
from sklearn import tree
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
from sklearn.tree import export_graphviz
from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier
	import pandas as pd
	from random import random

	flow = (list(range(1,10,1)) + list(range(10,1,-1)))*1000
	pdata = pd.DataFrame({"a":flow, "b":flow})
	pdata.b = pdata.b.shift(9)
	data = pdata.iloc[10:] * random() # some noise

	import numpy as np
	""" From: http://danielhnyk.cz/predicting-sequences-vectors-keras-using-rnn-lstm/ """
	from keras.models import Sequential
	from keras.layers.core import TimeDistributedDense, Activation, Dropout
	from keras.layers.recurrent import GRU
	import numpy as np

	def _load_data(data, steps = 40):
	docX, docY = [], []
	for i in range(0, data.shape[0]/steps-1):
	docX.append(data[isteps:(i+1)steps,:])
	# Some code to asses an SVM with a two-dimensional feature space:
	# trend (price - simple moving average) and relative strength index.
	# The code is an adaptation of the code found in the following linkedin post:
	# "Trading the RSI using a Support Vector Machine"
	# "https://www.linkedin.com/pulse/article/20141103165037-172934333-trading-the-rsi-using-a-support-vector-machine"

	# Settings
	sma.window = 50 # Number of observations in simple moving average.
	rsi.window = 3 # Number of observation in relative strength index (RSI)
	library(quantmod)
	getSymbols(c("GDP", "CMDEBT"), src="FRED")
	plot(cumsum(na.omit(diff(GDP) - diff(CMDEBT))))
	import os
	import io
	import subprocess
	import pandas as pd
	import numpy as np
	from sklearn import tree
	from sklearn import preprocessing
	from sklearn.cross_validation import train_test_split
	from sklearn.tree import export_graphviz
	from sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier