Now it takes 30 minutes to build the dataset and 5 minutes to make new predictions
In [1]:
from __future__ import print_function
import sys, os, requests, json, datetime
# Load the environment and login the user
from src.common.load_redten_ipython_env import user_token, user_login, csv_file, run_job, core, api_urls, ppj, rt_url, rt_user, rt_pass, rt_email, lg, good, boom, anmt, mark, ppj, uni_key, rest_login_as_user, rest_full_login, wait_for_job_to_finish, wait_on_job, get_job_analysis, get_job_results, get_analysis_manifest, get_job_cache_manifest, build_prediction_results, build_forecast_results, get_job_cache_manifest, search_ml_jobs, show_logs, show_errors, ipyImage, ipyHTML, ipyDisplay, pd, np
In [2]:
# dataset name is the ticker
ds_name = "SPY"
# Label and description for job
title = str(ds_name) + " Forecast v5 - " + str(uni_key())
desc = "Forecast simulation - " + str(datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
# Whats the algorithm model you want to use?
algo_name = "xgb-regressor"
# If your dataset is stored in redis, you can pass in the location
# to the dataset like: <redis endpoint>:<key>
rloc = ""
# If your dataset is stored in S3, you can pass in the location
# to the dataset like: <bucket>:<key>
sloc = ""
# During training what ratio of tests-vs-training do you want to use?
# Trade off smarts vs accuracy...how smart are we going?
test_ratio = 0.1
# Customize dataset samples used during the analysis using json dsl
sample_filter_rules = {}
# What column do you want to predict values?
target_column_name = "FClose"
# What columns can the algorithms use for training and learning?
feature_column_names = [ "FHigh", "FLow", "FOpen", "FClose", "FVolume" ]
# values in the Target Column
target_column_values = [ "GoodBuys", "BadBuys", "Not Finished" ]
# How many units ahead do you want to forecast?
units_ahead_set = [ 5, 10, 15, 20, 25, 30 ]
units_ahead_type = "Days"
# Prune non-int/float columns as needed:
ignore_features = [
"Ticker",
"Date",
"FDate",
"FPrice",
"DcsnDate",
"Decision"
]
# Set up the XGB parameter
# https://github.com/dmlc/xgboost/blob/master/doc/parameter.md
train_xgb = {
"learning_rate" : 0.20,
"num_estimators" : 50,
"sub_sample" : 0.20,
"col_sample_by_tree" : 0.90,
"col_sample_by_level" : 1.0,
"objective" : "reg:linear",
"max_depth" : 3,
"max_delta_step" : 0,
"min_child_weight" : 1,
"reg_alpha" : 0,
"reg_lambda" : 1,
"base_score" : 0.6,
"gamma" : 0,
"seed" : 42,
"silent" : True
}
# Predict new price points during the day
predict_row = {
"High" : 250.82,
"Low" : 245.54,
"Open" : 247.77,
"Close" : 246.24,
"Volume" : 77670266
}
In [3]:
job_id = None # on success, this will store the actively running job's id
csv_file = ""
post_data = {
"predict_this_data" : predict_row,
"title" : title,
"desc" : desc,
"ds_name" : ds_name,
"target_column_name" : target_column_name,
"feature_column_names" : feature_column_names,
"ignore_features" : ignore_features,
"csv_file" : csv_file,
"rloc" : rloc,
"sloc" : sloc,
"algo_name" : algo_name,
"test_ratio" : test_ratio,
"target_column_values" : target_column_values,
"label_column_name" : target_column_name,
"prediction_type" : "Forecast",
"ml_type" : "Playbook-UnitsAhead",
"train" : train_xgb,
"tracking_type" : "",
"units_ahead_set" : units_ahead_set,
"units_ahead_type" : units_ahead_type,
"forecast_type" : "ETFPriceForecasting",
"sample_filters" : sample_filter_rules,
"predict_units_back" : 90, # how many days back should the final chart go?
"send_to_email" : [ "jay.p.h.johnson@gmail.com" ] # comma separated list
}
anmt("Running job: " + str(title))
auth_headers = {
"Content-type": "application/json",
"Authorization" : "JWT " + str(user_token)
}
job_response = run_job(post_data=post_data, headers=auth_headers)
if job_response["status"] != "valid":
boom("Forecast job failed with error=" + str(job_response["status"]))
else:
if "id" not in job_response["data"]:
boom("Failed to create new forecast job")
else:
job_id = job_response["data"]["id"]
job_status = job_response["data"]["status"]
lg("Started Forecast job=" + str(job_id) + " with current status=" + str(job_status))
# end of if job was valid or not
Running job: SPY Forecast v5 - 34d6bf175cad4aaf9234aa06e95f4e4
Started Forecast job=558 with current status=requested
In [4]:
job_data = {}
job_report = {}
# Should hook this up to a randomized image loader...
ipyDisplay(ipyImage(url="https://media.giphy.com/media/l397998l2DT0ogare/giphy.gif"))
job_res = {}
if job_id == None:
boom("Failed to start a new job")
else:
job_res = wait_on_job(job_id)
if job_res["status"] != "SUCCESS":
boom("Job=" + str(job_id) + " failed with status=" + str(job_res["status"]) + " err=" + str(job_res["error"]))
else:
job_data = job_res["record"]
anmt("Job Report:")
lg(ppj(job_data), 5)
# end of waiting
Waiting on job=558 url=https://redten.io/ml/558/
Job=558 is training - Step 3/10
Job=558 is analyzing - Step 5/10
Job=558 is caching - Step 6/10
Job=558 is plotting - Step 7/10
Job=558 is uploading - Step 9/10
Job=558 completed
Job Report:
{
"job": {
"algo_name": "xgb-regressor",
"control_state": "active",
"created": "2017-05-26 08-02-13",
"csv_file": "",
"desc": "Forecast simulation - 2017-05-26 08:02:13",
"ds_name": "SPY",
"feature_column_names": [
"FHigh",
"FLow",
"FOpen",
"FClose",
"FVolume"
],
"id": 558,
"ignore_features": [
"Ticker",
"Date",
"FDate",
"FPrice",
"DcsnDate",
"Decision"
],
"images": [
{
"author_name": null,
"desc": null,
"id": 12122,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12122_7c49dccd964548fc.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 5-Days - Predictive Accuracy\nPredicted Close 5 Days vs Actual Close 5 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12121,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12121_5ae84fdafa034174.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 5-Days - Predictive Accuracy\nPredicted High 5 Days vs Actual High 5 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12120,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12120_2d87f32772344921.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 10-Days - Predictive Accuracy\nPredicted Close 10 Days vs Actual Close 10 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12119,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12119_c54a1003037d4eb3.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 15-Days - Predictive Accuracy\nPredicted Close 15 Days vs Actual Close 15 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12118,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12118_192e7a3c4ba840fc.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 30-Days - Predictive Accuracy\nPredicted Close 30 Days vs Actual Close 30 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12117,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12117_6b74cc14075f4f20.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 25-Days - Predictive Accuracy\nPredicted Low 25 Days vs Actual Low 25 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12116,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12116_1fed112e3a4e42ff.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 20-Days - Predictive Accuracy\nPredicted Low 20 Days vs Actual Low 20 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12115,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12115_795d9657c2534f76.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 15-Days - Predictive Accuracy\nPredicted Volume 15 Days vs Actual Volume 15 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12114,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12114_c19203024ec84944.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 30-Days - Predictive Accuracy\nPredicted Volume 30 Days vs Actual Volume 30 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12113,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12113_1b000088cf154451.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 10-Days - Predictive Accuracy\nPredicted Volume 10 Days vs Actual Volume 10 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12112,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12112_c72e079979774d17.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 20-Days - Predictive Accuracy\nPredicted Open 20 Days vs Actual Open 20 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12111,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12111_b0c21c4c4d0d4e7a.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 30-Days - Predictive Accuracy\nPredicted High 30 Days vs Actual High 30 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12110,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12110_2a27883839464a6c.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 25-Days - Predictive Accuracy\nPredicted Open 25 Days vs Actual Open 25 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12109,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12109_325e5fefa07a444d.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 15-Days - Predictive Accuracy\nPredicted High 15 Days vs Actual High 15 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12108,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12108_7dfe5cad6dd341c2.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 10-Days - Predictive Accuracy\nPredicted High 10 Days vs Actual High 10 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12107,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12107_2fcaa612a10346d9.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 15-Days - Predictive Accuracy\nPredicted Low 15 Days vs Actual Low 15 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12106,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12106_29c5e186270348f4.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 5-Days - Predictive Accuracy\nPredicted Open 5 Days vs Actual Open 5 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12105,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12105_7139a2dd4df5447b.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 10-Days - Predictive Accuracy\nPredicted Low 10 Days vs Actual Low 10 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12104,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12104_74d4b72e2bd342d6.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 25-Days - Predictive Accuracy\nPredicted Close 25 Days vs Actual Close 25 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12103,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12103_286431b3eabc4af5.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 5-Days - Predictive Accuracy\nPredicted Volume 5 Days vs Actual Volume 5 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12102,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12102_b8f6174000f5455c.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 20-Days - Predictive Accuracy\nPredicted Close 20 Days vs Actual Close 20 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12101,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12101_bfe9888ad15648f9.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 30-Days - Predictive Accuracy\nPredicted Low 30 Days vs Actual Low 30 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12100,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12100_0761ea1b0f684ce9.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 25-Days - Predictive Accuracy\nPredicted Volume 25 Days vs Actual Volume 25 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12099,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12099_8f73f41290794595.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 20-Days - Predictive Accuracy\nPredicted Volume 20 Days vs Actual Volume 20 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12098,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12098_5fd3e28826754f24.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 20-Days - Predictive Accuracy\nPredicted High 20 Days vs Actual High 20 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12097,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12097_214858c79e704f4c.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 30-Days - Predictive Accuracy\nPredicted Open 30 Days vs Actual Open 30 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12096,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12096_168bcf41ff5142ad.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 25-Days - Predictive Accuracy\nPredicted High 25 Days vs Actual High 25 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12095,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12095_de06fd5548504b41.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 15-Days - Predictive Accuracy\nPredicted Open 15 Days vs Actual Open 15 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12094,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12094_354bb5267c694ba5.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 5-Days - Predictive Accuracy\nPredicted Low 5 Days vs Actual Low 5 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12093,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12093_e2ecdc03bc8a4f30.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY-2-558 10-Days - Predictive Accuracy\nPredicted Open 10 Days vs Actual Open 10 Days",
"version": 1
},
{
"author_name": null,
"desc": null,
"id": 12092,
"image": "https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12092_954ad32f51ef47f4.png",
"json_data": null,
"label": null,
"shareable_link": null,
"sloc": null,
"status": "initial",
"title": "SPY Close forecast overlay between 2017-02-27 00:00:00 - 2017-05-15 00:00:00",
"version": 1
}
],
"manifest_secret_key": "live_2_f3f31f711f1b4081862a1927be0758880c3b3f00e2848aeb2a7f68997b4e17",
"manifest_sloc": "redten-models-west:SPY-2-558_manifest.json",
"max_features": 10,
"ml_type": "Playbook-UnitsAhead",
"prediction_type": "forecast",
"rloc": "",
"sloc": "",
"status": "completed",
"target_column_name": "FClose",
"target_column_values": [
"GoodBuys",
"BadBuys",
"Not Finished"
],
"title": "SPY Forecast v5 - 34d6bf175cad4aaf9234aa06e95f4e4",
"units_ahead_set": [
5,
10,
15,
20,
25,
30
],
"units_ahead_type": "Days",
"updated": "2017-05-26 08-03-53",
"version": 1
}
}
In [5]:
job_report = {}
if job_id == None:
boom("Failed to start a new job")
else:
# Get the analysis, but do not auto-show the plots
job_report = get_job_analysis(job_id, show_plots=False)
if len(job_report) == 0:
boom("Job=" + str(job_id) + " failed")
else:
lg("")
# if the job failed
# end of get job analysis
# Build the forecast accuracy dictionary from the analysis
# and show the forecast dataframes
acc_results = build_forecast_results(job_report)
for col in acc_results:
col_node = acc_results[col]
predictions_df = col_node["predictions_df"]
date_predictions_df = col_node["date_predictions_df"]
train_predictions_df = col_node["train_predictions_df"]
lg("--------------------------------------------------")
# for all columns in the accuracy dictionary:
# successful predictions above 90%...how's that error rate though?
if col_node["accuracy"] > 0.90:
good("Column=" + str(col) + " accuracy=" + str(col_node["accuracy"]) + " mse=" + str(col_node["mse"]) + " num_predictions=" + str(len(col_node["date_predictions_df"].index)))
# successful predictions between 90% and 80%...how's that error rate though?
elif 0.90 > col_node["accuracy"] > 0.80:
lg("Column=" + str(col) + " accuracy=" + str(col_node["accuracy"]) + " mse=" + str(col_node["mse"]) + " num_predictions=" + str(len(col_node["date_predictions_df"].index)))
else:
boom("Column=" + str(col) + " is not very accurate: accuracy=" + str(col_node["accuracy"]) + " mse=" + str(col_node["mse"]) + " num_predictions=" + str(len(col_node["predictions_df"].index)))
# end of header line
# show the timeseries forecast
ipyDisplay(date_predictions_df)
lg("")
# end of showing prediction results
Getting analysis for job=558 url=https://redten.io/ml/analysis/558/
SUCCESS - GET Analysis Response Status=200 Reason=OK
Found Job=558 analysis
--------------------------------------------------
Column=FOpen_10 accuracy=0.99848923125 mse=1.87511875363 num_predictions=50
COpen
Date
FOpen
0
236.64
2017-02-27
237.073318
1
236.67
2017-02-28
236.157669
2
238.39
2017-03-01
237.216782
3
239.56
2017-03-02
235.210175
4
238.17
2017-03-03
233.699661
5
237.50
2017-03-06
237.479095
6
237.71
2017-03-07
235.920334
7
237.34
2017-03-08
237.982605
8
236.70
2017-03-09
237.568695
9
237.97
2017-03-10
235.905457
10
237.62
2017-03-13
236.509735
11
237.18
2017-03-14
234.706680
12
237.56
2017-03-15
236.196335
13
239.11
2017-03-16
235.618851
14
237.75
2017-03-17
236.832428
15
237.03
2017-03-20
236.587128
16
237.47
2017-03-21
237.348419
17
233.77
2017-03-22
237.528641
18
234.00
2017-03-23
234.676941
19
234.38
2017-03-24
236.232239
20
231.93
2017-03-27
235.156250
21
233.27
2017-03-28
234.025803
22
234.99
2017-03-29
234.184830
23
235.47
2017-03-30
233.101852
24
235.90
2017-03-31
237.531754
25
235.80
2017-04-03
233.742493
26
234.95
2017-04-04
235.229843
27
236.26
2017-04-05
236.990433
28
234.94
2017-04-06
237.611145
29
235.15
2017-04-07
234.539597
30
235.36
2017-04-10
237.734253
31
234.90
2017-04-11
236.622452
32
234.74
2017-04-12
234.549881
33
233.64
2017-04-13
236.684601
34
233.64
2017-04-14
237.827515
35
233.11
2017-04-17
235.516617
36
233.72
2017-04-18
236.572464
37
234.52
2017-04-19
237.910950
38
235.25
2017-04-21
237.734253
39
237.18
2017-04-24
233.342529
40
237.91
2017-04-25
237.520111
41
238.51
2017-04-26
232.563446
42
238.77
2017-04-27
234.030930
43
238.90
2017-04-28
234.826447
44
238.68
2017-05-01
236.066803
45
238.84
2017-05-02
235.076416
46
238.29
2017-05-03
233.416733
47
238.83
2017-05-04
236.058029
48
239.19
2017-05-05
235.863937
49
239.75
2017-05-08
235.429077
--------------------------------------------------
Column=FLow_5 accuracy=0.998086039927 mse=1.85004769663 num_predictions=55
CLow
Date
FLow
0
236.35
2017-02-27
236.929581
1
236.02
2017-02-28
236.010742
2
238.37
2017-03-01
233.294571
3
238.21
2017-03-02
232.308624
4
237.73
2017-03-03
234.968628
5
237.01
2017-03-06
236.273605
6
237.71
2017-03-07
236.508759
7
236.40
2017-03-08
233.406143
8
235.74
2017-03-09
235.658737
9
236.59
2017-03-10
231.818314
10
237.24
2017-03-13
234.524475
11
236.19
2017-03-14
235.794052
12
237.29
2017-03-15
233.459610
13
238.10
2017-03-16
233.198196
14
237.03
2017-03-17
237.028275
15
236.32
2017-03-20
233.921158
16
233.58
2017-03-21
235.768692
17
233.05
2017-03-22
236.422226
18
233.60
2017-03-23
236.376251
19
232.96
2017-03-24
233.915695
20
231.61
2017-03-27
236.468430
21
233.14
2017-03-28
236.915192
22
234.72
2017-03-29
235.886642
23
235.27
2017-03-30
233.052322
24
235.68
2017-03-31
235.961349
25
233.91
2017-04-03
232.261780
26
234.56
2017-04-04
234.373062
27
234.54
2017-04-05
233.631317
28
234.42
2017-04-06
234.610016
29
234.64
2017-04-07
236.890594
30
234.73
2017-04-10
234.642807
31
233.34
2017-04-11
234.695312
32
233.77
2017-04-12
233.865128
33
232.51
2017-04-13
236.446716
34
232.51
2017-04-14
235.104309
35
232.88
2017-04-17
233.097855
36
233.08
2017-04-18
236.518036
37
233.18
2017-04-19
236.571960
38
234.13
2017-04-21
234.559448
39
234.56
2017-04-24
236.496582
40
237.81
2017-04-25
236.412048
41
238.35
2017-04-26
236.186478
42
237.98
2017-04-27
235.107819
43
237.93
2017-04-28
233.527512
44
238.20
2017-05-01
236.808197
45
238.30
2017-05-02
236.269989
46
237.70
2017-05-03
235.095825
47
237.78
2017-05-04
234.935760
48
238.68
2017-05-05
235.331024
49
239.17
2017-05-08
234.041565
50
239.04
2017-05-09
236.181931
51
239.04
2017-05-10
236.355286
52
238.13
2017-05-11
232.871155
53
238.67
2017-05-12
233.601791
54
239.45
2017-05-15
236.600067
--------------------------------------------------
Column=FOpen_15 accuracy=0.998673216637 mse=1.36712346617 num_predictions=45
COpen
Date
FOpen
0
236.64
2017-02-27
238.666595
1
236.67
2017-02-28
237.613739
2
238.39
2017-03-01
237.458069
3
239.56
2017-03-02
236.777634
4
238.17
2017-03-03
238.085297
5
237.50
2017-03-06
237.372849
6
237.71
2017-03-07
234.226608
7
237.34
2017-03-08
235.627625
8
236.70
2017-03-09
236.552750
9
237.97
2017-03-10
234.387222
10
237.62
2017-03-13
234.385269
11
237.18
2017-03-14
234.300766
12
237.56
2017-03-15
238.415405
13
239.11
2017-03-16
234.556824
14
237.75
2017-03-17
235.208084
15
237.03
2017-03-20
235.048111
16
237.47
2017-03-21
236.497421
17
233.77
2017-03-22
234.135391
18
234.00
2017-03-23
234.963669
19
234.38
2017-03-24
234.820038
20
231.93
2017-03-27
237.683395
21
233.27
2017-03-28
238.560837
22
234.99
2017-03-29
238.095673
23
235.47
2017-03-30
236.939590
24
235.90
2017-03-31
237.795105
25
235.80
2017-04-03
237.874680
26
234.95
2017-04-04
233.943573
27
236.26
2017-04-05
234.943817
28
234.94
2017-04-06
238.051514
29
235.15
2017-04-07
235.384384
30
235.36
2017-04-10
237.187256
31
234.90
2017-04-11
234.732925
32
234.74
2017-04-12
233.787415
33
233.64
2017-04-13
238.512772
34
233.64
2017-04-14
234.131561
35
233.11
2017-04-17
238.079544
36
233.72
2017-04-18
233.760864
37
234.52
2017-04-19
234.805496
38
235.25
2017-04-21
234.080505
39
237.18
2017-04-24
236.442703
40
237.91
2017-04-25
233.432205
41
238.51
2017-04-26
234.990753
42
238.77
2017-04-27
236.403168
43
238.90
2017-04-28
235.337692
44
238.68
2017-05-01
237.205765
--------------------------------------------------
Column=FHigh_25 accuracy=0.99852625367 mse=2.07955530003 num_predictions=35
CHigh
Date
FHigh
0
237.31
2017-02-27
238.076721
1
236.95
2017-02-28
234.146927
2
240.32
2017-03-01
237.973175
3
239.57
2017-03-02
237.950897
4
238.61
2017-03-03
237.457062
5
238.12
2017-03-06
238.205643
6
237.71
2017-03-07
237.868561
7
237.64
2017-03-08
233.265198
8
237.24
2017-03-09
234.387634
9
238.02
2017-03-10
237.551636
10
237.86
2017-03-13
235.177246
11
237.24
2017-03-14
237.380798
12
239.44
2017-03-15
234.876892
13
239.20
2017-03-16
235.736435
14
237.97
2017-03-17
238.128571
15
237.36
2017-03-20
237.794815
16
237.61
2017-03-21
237.752106
17
234.61
2017-03-22
236.209839
18
235.34
2017-03-23
234.751801
19
235.04
2017-03-24
234.983871
20
233.92
2017-03-27
238.090561
21
235.81
2017-03-28
233.964142
22
235.81
2017-03-29
237.529388
23
236.52
2017-03-30
234.443344
24
236.51
2017-03-31
238.910355
25
236.03
2017-04-03
237.520447
26
235.58
2017-04-04
237.694489
27
237.39
2017-04-05
234.321915
28
236.00
2017-04-07
238.316208
29
236.26
2017-04-10
237.038849
30
235.18
2017-04-11
237.573196
31
234.96
2017-04-12
235.150558
32
234.49
2017-04-13
238.119049
33
234.49
2017-04-14
234.923492
34
234.57
2017-04-17
235.094742
--------------------------------------------------
Column=FOpen_30 accuracy=0.998227319751 mse=3.31048708482 num_predictions=30
COpen
Date
FOpen
0
236.64
2017-02-27
234.693985
1
236.67
2017-02-28
236.030411
2
238.39
2017-03-01
235.485245
3
239.56
2017-03-02
237.560745
4
238.17
2017-03-03
235.975784
5
237.50
2017-03-06
235.504822
6
237.34
2017-03-08
235.342697
7
236.70
2017-03-09
236.801010
8
237.97
2017-03-10
237.499802
9
237.62
2017-03-13
234.917587
10
237.18
2017-03-14
233.616501
11
237.56
2017-03-15
237.476257
12
239.11
2017-03-16
232.399368
13
237.75
2017-03-17
236.711685
14
237.03
2017-03-20
236.574722
15
237.47
2017-03-21
237.442719
16
233.77
2017-03-22
234.120331
17
234.00
2017-03-23
233.750183
18
234.38
2017-03-24
237.016220
19
231.93
2017-03-27
232.151428
20
233.27
2017-03-28
236.966064
21
234.99
2017-03-29
237.586349
22
235.47
2017-03-30
236.353043
23
235.90
2017-03-31
237.411346
24
235.80
2017-04-03
238.304245
25
234.95
2017-04-04
235.202972
26
236.26
2017-04-05
236.926498
27
234.94
2017-04-06
237.732910
28
235.15
2017-04-07
236.833130
29
235.36
2017-04-10
238.301956
--------------------------------------------------
Column=FHigh_20 accuracy=0.99830741251 mse=1.25659633163 num_predictions=40
CHigh
Date
FHigh
0
237.31
2017-02-27
237.670395
1
236.95
2017-02-28
235.000000
2
240.32
2017-03-01
235.114883
3
239.57
2017-03-02
238.374283
4
238.61
2017-03-03
236.344513
5
238.12
2017-03-06
237.315735
6
237.71
2017-03-07
236.214111
7
237.64
2017-03-08
237.246307
8
237.24
2017-03-09
238.396393
9
238.02
2017-03-10
236.479446
10
237.86
2017-03-13
237.763672
11
237.24
2017-03-14
235.117249
12
239.44
2017-03-15
234.345337
13
239.20
2017-03-16
236.225082
14
237.97
2017-03-17
234.697128
15
237.36
2017-03-20
238.262817
16
237.61
2017-03-21
235.133362
17
234.61
2017-03-22
238.553192
18
235.34
2017-03-23
238.866257
19
235.04
2017-03-24
233.853409
20
233.92
2017-03-27
236.915115
21
235.81
2017-03-28
236.299088
22
235.81
2017-03-29
234.427261
23
236.52
2017-03-30
235.588684
24
236.51
2017-03-31
237.638855
25
236.03
2017-04-03
238.629776
26
235.58
2017-04-04
234.676880
27
237.39
2017-04-05
238.158661
28
236.04
2017-04-06
238.226105
29
236.00
2017-04-07
235.759537
30
236.26
2017-04-10
238.553192
31
235.18
2017-04-11
237.456482
32
234.96
2017-04-12
238.249573
33
234.49
2017-04-13
235.751602
34
234.49
2017-04-14
237.794708
35
234.57
2017-04-17
235.746872
36
234.49
2017-04-18
237.754822
37
234.95
2017-04-19
236.131210
38
235.31
2017-04-21
235.008286
39
237.41
2017-04-24
238.001404
--------------------------------------------------
Column=FVolume_20 accuracy=0.908394205285 mse=1.16457020726e+14 num_predictions=40
CVolume
Date
FVolume
0
56515440.0
2017-02-27
65846012.0
1
96961938.0
2017-02-28
83015680.0
2
149158170.0
2017-03-01
64974404.0
3
70245978.0
2017-03-02
68879856.0
4
81974300.0
2017-03-03
66677188.0
5
55391533.0
2017-03-06
101378352.0
6
393822.0
2017-03-07
99078096.0
7
78168795.0
2017-03-08
97593016.0
8
90683918.0
2017-03-09
64936852.0
9
81991652.0
2017-03-10
95077096.0
10
57256824.0
2017-03-13
61924532.0
11
59880778.0
2017-03-14
83244072.0
12
96081750.0
2017-03-15
76859560.0
13
78343951.0
2017-03-16
63056964.0
14
89002111.0
2017-03-17
62771728.0
15
52536979.0
2017-03-20
62433760.0
16
131809275.0
2017-03-21
79715272.0
17
97569204.0
2017-03-22
57779620.0
18
100410277.0
2017-03-23
67151176.0
19
112504853.0
2017-03-24
89839216.0
20
87454452.0
2017-03-27
81388680.0
21
93483915.0
2017-03-28
84004696.0
22
61950354.0
2017-03-29
89794744.0
23
56737890.0
2017-03-30
87538128.0
24
73733094.0
2017-03-31
57832536.0
25
85546486.0
2017-04-03
77662536.0
26
56466195.0
2017-04-04
80301888.0
27
108800604.0
2017-04-05
64505432.0
28
69135757.0
2017-04-06
155792864.0
29
74412311.0
2017-04-07
59956728.0
30
67615302.0
2017-04-10
63884772.0
31
88045276.0
2017-04-11
61656740.0
32
81864436.0
2017-04-12
46140620.0
33
92880394.0
2017-04-13
72249024.0
34
92880394.0
2017-04-14
83007760.0
35
68405367.0
2017-04-17
83554112.0
36
83225821.0
2017-04-18
107252736.0
37
68699868.0
2017-04-19
107173648.0
38
110389847.0
2017-04-21
74443776.0
39
119209877.0
2017-04-24
55639312.0
--------------------------------------------------
Column=FVolume_25 accuracy=0.875114335535 mse=5.61678331942e+13 num_predictions=35
CVolume
Date
FVolume
0
56515440.0
2017-02-27
109613136.0
1
96961938.0
2017-02-28
84890608.0
2
149158170.0
2017-03-01
56550484.0
3
70245978.0
2017-03-02
64852992.0
4
81974300.0
2017-03-03
78665056.0
5
55391533.0
2017-03-06
62343776.0
6
393822.0
2017-03-07
65068232.0
7
78168795.0
2017-03-08
73619968.0
8
90683918.0
2017-03-09
86562520.0
9
81991652.0
2017-03-10
83784728.0
10
57256824.0
2017-03-13
70358608.0
11
59880778.0
2017-03-14
94782192.0
12
96081750.0
2017-03-15
90527832.0
13
78343951.0
2017-03-16
82991488.0
14
89002111.0
2017-03-17
58278716.0
15
52536979.0
2017-03-20
112854176.0
16
131809275.0
2017-03-21
55025048.0
17
97569204.0
2017-03-22
66197604.0
18
100410277.0
2017-03-23
75862600.0
19
112504853.0
2017-03-24
65388524.0
20
87454452.0
2017-03-27
67723848.0
21
93483915.0
2017-03-28
113617928.0
22
61950354.0
2017-03-29
94782192.0
23
56737890.0
2017-03-30
62635744.0
24
73733094.0
2017-03-31
164215552.0
25
85546486.0
2017-04-03
60994780.0
26
56466195.0
2017-04-04
57976436.0
27
108800604.0
2017-04-05
60855060.0
28
74412311.0
2017-04-07
61561904.0
29
67615302.0
2017-04-10
60636220.0
30
88045276.0
2017-04-11
66711616.0
31
81864436.0
2017-04-12
99195736.0
32
92880394.0
2017-04-13
68778016.0
33
92880394.0
2017-04-14
74303160.0
34
68405367.0
2017-04-17
109592048.0
--------------------------------------------------
Column=FLow_30 accuracy=0.998277970848 mse=2.96286991645 num_predictions=30
CLow
Date
FLow
0
236.35
2017-02-27
235.590820
1
236.02
2017-02-28
236.062424
2
238.37
2017-03-01
235.890472
3
238.21
2017-03-02
236.847061
4
237.73
2017-03-03
234.398666
5
237.01
2017-03-06
234.975235
6
236.40
2017-03-08
234.611038
7
235.74
2017-03-09
235.617630
8
236.59
2017-03-10
236.238098
9
237.24
2017-03-13
233.991272
10
236.19
2017-03-14
233.256729
11
237.29
2017-03-15
236.445068
12
238.10
2017-03-16
233.417892
13
237.03
2017-03-17
235.901810
14
236.32
2017-03-20
236.761841
15
233.58
2017-03-21
237.303406
16
233.05
2017-03-22
233.292572
17
233.60
2017-03-23
231.795105
18
232.96
2017-03-24
236.326782
19
231.61
2017-03-27
233.716980
20
233.14
2017-03-28
237.041916
21
234.72
2017-03-29
236.383987
22
235.27
2017-03-30
237.199799
23
235.68
2017-03-31
235.502136
24
233.91
2017-04-03
236.134262
25
234.56
2017-04-04
233.488770
26
234.54
2017-04-05
235.824600
27
234.42
2017-04-06
236.688004
28
234.64
2017-04-07
234.916611
29
234.73
2017-04-10
236.598770
--------------------------------------------------
Column=FClose_20 accuracy=0.998412598936 mse=1.17535948971 num_predictions=40
CClose
Date
FClose
0
237.11
2017-02-27
237.442490
1
236.47
2017-02-28
232.841202
2
239.78
2017-03-01
235.384415
3
238.27
2017-03-02
237.826065
4
238.42
2017-03-03
236.225693
5
237.71
2017-03-06
237.156509
6
237.71
2017-03-07
236.379303
7
236.56
2017-03-08
236.556427
8
236.86
2017-03-09
238.205902
9
237.69
2017-03-10
234.902100
10
237.81
2017-03-13
237.537613
11
236.90
2017-03-14
234.018509
12
238.95
2017-03-15
234.722977
13
238.48
2017-03-16
235.003632
14
237.03
2017-03-17
234.271652
15
236.77
2017-03-20
237.974792
16
233.73
2017-03-21
234.114609
17
234.28
2017-03-22
238.024323
18
234.03
2017-03-23
237.974792
19
233.86
2017-03-24
233.514282
20
233.62
2017-03-27
237.471954
21
235.32
2017-03-28
235.134399
22
235.54
2017-03-29
234.860138
23
236.29
2017-03-30
234.930237
24
235.74
2017-03-31
237.970367
25
235.33
2017-04-03
237.838745
26
235.48
2017-04-04
234.534256
27
234.78
2017-04-05
237.864243
28
235.44
2017-04-06
238.316711
29
235.20
2017-04-07
235.626526
30
235.34
2017-04-10
238.132874
31
235.06
2017-04-11
237.999649
32
234.03
2017-04-12
237.994370
33
232.51
2017-04-13
235.509064
34
234.03
2017-04-14
237.547440
35
234.57
2017-04-17
234.448135
36
233.87
2017-04-18
237.110519
37
233.44
2017-04-19
236.106888
38
234.59
2017-04-21
234.382721
39
237.17
2017-04-24
237.820465
--------------------------------------------------
Column=FVolume_5 accuracy=0.860508546731 mse=1.54519726446e+14 num_predictions=55
CVolume
Date
FVolume
0
56515440.0
2017-02-27
47996392.0
1
96961938.0
2017-02-28
46576272.0
2
149158170.0
2017-03-01
84940464.0
3
70245978.0
2017-03-02
90956808.0
4
81974300.0
2017-03-03
74963216.0
5
55391533.0
2017-03-06
55247800.0
6
393822.0
2017-03-07
69506984.0
7
78168795.0
2017-03-08
78182704.0
8
90683918.0
2017-03-09
51209964.0
9
81991652.0
2017-03-10
72567152.0
10
57256824.0
2017-03-13
97008752.0
11
59880778.0
2017-03-14
72061368.0
12
96081750.0
2017-03-15
97580816.0
13
78343951.0
2017-03-16
62746008.0
14
89002111.0
2017-03-17
69350312.0
15
52536979.0
2017-03-20
62146480.0
16
131809275.0
2017-03-21
82602136.0
17
97569204.0
2017-03-22
60284568.0
18
100410277.0
2017-03-23
61597948.0
19
112504853.0
2017-03-24
76730536.0
20
87454452.0
2017-03-27
49964956.0
21
93483915.0
2017-03-28
47066652.0
22
61950354.0
2017-03-29
177215760.0
23
56737890.0
2017-03-30
89014920.0
24
73733094.0
2017-03-31
69948528.0
25
85546486.0
2017-04-03
69864920.0
26
56466195.0
2017-04-04
52336716.0
27
108800604.0
2017-04-05
74350704.0
28
69135757.0
2017-04-06
113147264.0
29
74412311.0
2017-04-07
44125816.0
30
67615302.0
2017-04-10
76967448.0
31
88045276.0
2017-04-11
109703480.0
32
81864436.0
2017-04-12
71968256.0
33
92880394.0
2017-04-13
55783700.0
34
92880394.0
2017-04-14
70136248.0
35
68405367.0
2017-04-17
76711440.0
36
83225821.0
2017-04-18
59971112.0
37
68699868.0
2017-04-19
9440932.0
38
110389847.0
2017-04-21
66461792.0
39
119209877.0
2017-04-24
67064808.0
40
76698265.0
2017-04-25
59714204.0
41
84702455.0
2017-04-26
67836168.0
42
57410326.0
2017-04-27
118460488.0
43
63532845.0
2017-04-28
53469000.0
44
66882521.0
2017-05-01
46206144.0
45
57375732.0
2017-05-02
42782120.0
46
73137731.0
2017-05-03
100944264.0
47
61462732.0
2017-05-04
76188488.0
48
62001269.0
2017-05-05
99223600.0
49
48385730.0
2017-05-08
77355240.0
50
51363200.0
2017-05-09
80711072.0
51
51363200.0
2017-05-10
63145664.0
52
23215447.0
2017-05-11
90556112.0
53
51877428.0
2017-05-12
77122408.0
54
60025663.0
2017-05-15
45219076.0
--------------------------------------------------
Column=FClose_25 accuracy=0.99845288618 mse=2.04919274668 num_predictions=35
CClose
Date
FClose
0
237.11
2017-02-27
236.552734
1
236.47
2017-02-28
235.050507
2
239.78
2017-03-01
237.163391
3
238.27
2017-03-02
237.538895
4
238.42
2017-03-03
236.731735
5
237.71
2017-03-06
237.555984
6
237.71
2017-03-07
237.320831
7
236.56
2017-03-08
234.358047
8
236.86
2017-03-09
233.667374
9
237.69
2017-03-10
237.382278
10
237.81
2017-03-13
234.445786
11
236.90
2017-03-14
235.993500
12
238.95
2017-03-15
234.081650
13
238.48
2017-03-16
235.814377
14
237.03
2017-03-17
237.346420
15
236.77
2017-03-20
235.385986
16
233.73
2017-03-21
237.190720
17
234.28
2017-03-22
234.789978
18
234.03
2017-03-23
233.903641
19
233.86
2017-03-24
235.736145
20
233.62
2017-03-27
237.013290
21
235.32
2017-03-28
235.373993
22
235.54
2017-03-29
236.336746
23
236.29
2017-03-30
235.648193
24
235.74
2017-03-31
236.748077
25
235.33
2017-04-03
236.493500
26
235.48
2017-04-04
237.717545
27
234.78
2017-04-05
233.553162
28
235.20
2017-04-07
236.987137
29
235.34
2017-04-10
236.417114
30
235.06
2017-04-11
237.215149
31
234.03
2017-04-12
235.193405
32
232.51
2017-04-13
238.073685
33
234.03
2017-04-14
235.692825
34
234.57
2017-04-17
234.849548
--------------------------------------------------
Column=FLow_10 accuracy=0.998567461387 mse=2.11792497821 num_predictions=50
CLow
Date
FLow
0
236.35
2017-02-27
237.413834
1
236.02
2017-02-28
237.225479
2
238.37
2017-03-01
237.645981
3
238.21
2017-03-02
234.386292
4
237.73
2017-03-03
233.403427
5
237.01
2017-03-06
237.240616
6
237.71
2017-03-07
235.407959
7
236.40
2017-03-08
236.065186
8
235.74
2017-03-09
236.531815
9
236.59
2017-03-10
235.872589
10
237.24
2017-03-13
235.920380
11
236.19
2017-03-14
232.937210
12
237.29
2017-03-15
233.090393
13
238.10
2017-03-16
233.984467
14
237.03
2017-03-17
234.309448
15
236.32
2017-03-20
235.186676
16
233.58
2017-03-21
238.271561
17
233.05
2017-03-22
236.720673
18
233.60
2017-03-23
235.235657
19
232.96
2017-03-24
235.274506
20
231.61
2017-03-27
234.975571
21
233.14
2017-03-28
232.597351
22
234.72
2017-03-29
235.073471
23
235.27
2017-03-30
233.283554
24
235.68
2017-03-31
236.843430
25
233.91
2017-04-03
233.786209
26
234.56
2017-04-04
234.607452
27
234.54
2017-04-05
236.101517
28
234.42
2017-04-06
236.949188
29
234.64
2017-04-07
234.220001
30
234.73
2017-04-10
237.824661
31
233.34
2017-04-11
235.214157
32
233.77
2017-04-12
234.126328
33
232.51
2017-04-13
236.780426
34
232.51
2017-04-14
236.256302
35
232.88
2017-04-17
234.450073
36
233.08
2017-04-18
233.575821
37
233.18
2017-04-19
236.969101
38
234.13
2017-04-21
236.524261
39
234.56
2017-04-24
233.803986
40
237.81
2017-04-25
237.522141
41
238.35
2017-04-26
233.729202
42
237.98
2017-04-27
233.496613
43
237.93
2017-04-28
235.620361
44
238.20
2017-05-01
233.358170
45
238.30
2017-05-02
235.508835
46
237.70
2017-05-03
233.407562
47
237.78
2017-05-04
236.723862
48
238.68
2017-05-05
235.565948
49
239.17
2017-05-08
235.229599
--------------------------------------------------
Column=FOpen_5 accuracy=0.998177191116 mse=2.27659209481 num_predictions=55
COpen
Date
FOpen
0
236.64
2017-02-27
238.192352
1
236.67
2017-02-28
236.068298
2
238.39
2017-03-01
233.407837
3
239.56
2017-03-02
233.426804
4
238.17
2017-03-03
234.870193
5
237.50
2017-03-06
237.075485
6
237.71
2017-03-07
237.380112
7
237.34
2017-03-08
234.219711
8
236.70
2017-03-09
237.943634
9
237.97
2017-03-10
233.489441
10
237.62
2017-03-13
235.329956
11
237.18
2017-03-14
235.384659
12
237.56
2017-03-15
234.191605
13
239.11
2017-03-16
234.631668
14
237.75
2017-03-17
238.205200
15
237.03
2017-03-20
234.193771
16
237.47
2017-03-21
236.639816
17
233.77
2017-03-22
236.817902
18
234.00
2017-03-23
236.664276
19
234.38
2017-03-24
234.296265
20
231.93
2017-03-27
237.186676
21
233.27
2017-03-28
237.978928
22
234.99
2017-03-29
235.493591
23
235.47
2017-03-30
234.852448
24
235.90
2017-03-31
235.815125
25
235.80
2017-04-03
233.627853
26
234.95
2017-04-04
234.599976
27
236.26
2017-04-05
234.378769
28
234.94
2017-04-06
235.113602
29
235.15
2017-04-07
236.804703
30
235.36
2017-04-10
234.353027
31
234.90
2017-04-11
235.246429
32
234.74
2017-04-12
233.361099
33
233.64
2017-04-13
237.659561
34
233.64
2017-04-14
237.646362
35
233.11
2017-04-17
234.575958
36
233.72
2017-04-18
237.595978
37
234.52
2017-04-19
236.852570
38
235.25
2017-04-21
234.651871
39
237.18
2017-04-24
234.744324
40
237.91
2017-04-25
237.875153
41
238.51
2017-04-26
237.987625
42
238.77
2017-04-27
236.527328
43
238.90
2017-04-28
233.824768
44
238.68
2017-05-01
237.369019
45
238.84
2017-05-02
235.249466
46
238.29
2017-05-03
234.652802
47
238.83
2017-05-04
234.766861
48
239.19
2017-05-05
235.063766
49
239.75
2017-05-08
234.963684
50
239.96
2017-05-09
236.949936
51
239.96
2017-05-10
236.464096
52
239.35
2017-05-11
234.053070
53
239.09
2017-05-12
234.455276
54
239.47
2017-05-15
236.910614
--------------------------------------------------
Column=FLow_15 accuracy=0.99868083036 mse=1.15577788337 num_predictions=45
CLow
Date
FLow
0
236.35
2017-02-27
237.317108
1
236.02
2017-02-28
236.028763
2
238.37
2017-03-01
236.467682
3
238.21
2017-03-02
235.270294
4
237.73
2017-03-03
237.266052
5
237.01
2017-03-06
236.366791
6
237.71
2017-03-07
233.293579
7
236.40
2017-03-08
234.402542
8
235.74
2017-03-09
235.478241
9
236.59
2017-03-10
233.113327
10
237.24
2017-03-13
233.244949
11
236.19
2017-03-14
235.251572
12
237.29
2017-03-15
237.382629
13
238.10
2017-03-16
232.962952
14
237.03
2017-03-17
234.481262
15
236.32
2017-03-20
234.740311
16
233.58
2017-03-21
235.420532
17
233.05
2017-03-22
234.751709
18
233.60
2017-03-23
235.780365
19
232.96
2017-03-24
234.348801
20
231.61
2017-03-27
237.593353
21
233.14
2017-03-28
237.389633
22
234.72
2017-03-29
237.485062
23
235.27
2017-03-30
236.241272
24
235.68
2017-03-31
237.196823
25
233.91
2017-04-03
236.735779
26
234.56
2017-04-04
232.439789
27
234.54
2017-04-05
234.444763
28
234.42
2017-04-06
237.228287
29
234.64
2017-04-07
235.283813
30
234.73
2017-04-10
237.159470
31
233.34
2017-04-11
233.135452
32
233.77
2017-04-12
232.638535
33
232.51
2017-04-13
237.368668
34
232.51
2017-04-14
233.268280
35
232.88
2017-04-17
237.489105
36
233.08
2017-04-18
235.493378
37
233.18
2017-04-19
232.950668
38
234.13
2017-04-21
232.923477
39
234.56
2017-04-24
236.022293
40
237.81
2017-04-25
233.592133
41
238.35
2017-04-26
234.243805
42
237.98
2017-04-27
236.920731
43
237.93
2017-04-28
235.522476
44
238.20
2017-05-01
236.753662
--------------------------------------------------
Column=FHigh_10 accuracy=0.998665333528 mse=3.19837957813 num_predictions=50
CHigh
Date
FHigh
0
237.31
2017-02-27
238.116806
1
236.95
2017-02-28
238.073212
2
240.32
2017-03-01
238.702911
3
239.57
2017-03-02
235.622223
4
238.61
2017-03-03
235.265427
5
238.12
2017-03-06
239.444489
6
237.71
2017-03-07
238.095032
7
237.64
2017-03-08
239.200668
8
237.24
2017-03-09
234.270569
9
238.02
2017-03-10
239.583817
10
237.86
2017-03-13
236.506302
11
237.24
2017-03-14
234.556320
12
239.44
2017-03-15
233.477707
13
239.20
2017-03-16
235.687210
14
237.97
2017-03-17
237.614822
15
237.36
2017-03-20
237.824234
16
237.61
2017-03-21
238.901703
17
234.61
2017-03-22
237.868256
18
235.34
2017-03-23
236.751755
19
235.04
2017-03-24
238.544586
20
233.92
2017-03-27
234.341949
21
235.81
2017-03-28
234.234421
22
235.81
2017-03-29
237.246872
23
236.52
2017-03-30
235.180527
24
236.51
2017-03-31
237.984360
25
236.03
2017-04-03
235.013977
26
235.58
2017-04-04
233.606445
27
237.39
2017-04-05
237.210892
28
236.04
2017-04-06
238.062119
29
236.00
2017-04-07
235.649841
30
236.26
2017-04-10
239.241165
31
235.18
2017-04-11
237.498764
32
234.96
2017-04-12
236.820740
33
234.49
2017-04-13
239.183060
34
234.49
2017-04-14
236.651443
35
234.57
2017-04-17
236.410843
36
234.49
2017-04-18
235.937378
37
234.95
2017-04-19
239.047195
38
235.31
2017-04-21
238.909409
39
237.41
2017-04-24
232.706223
40
238.95
2017-04-25
238.227890
41
239.53
2017-04-26
234.073669
42
238.95
2017-04-27
235.486847
43
238.93
2017-04-28
237.671921
44
239.17
2017-05-01
232.673920
45
238.98
2017-05-02
236.700531
46
238.88
2017-05-03
233.697357
47
238.92
2017-05-04
238.041336
48
239.72
2017-05-05
236.363312
49
239.92
2017-05-08
236.201157
--------------------------------------------------
Column=FHigh_15 accuracy=0.998541531411 mse=1.55878995885 num_predictions=45
CHigh
Date
FHigh
0
237.31
2017-02-27
237.879074
1
236.95
2017-02-28
237.778702
2
240.32
2017-03-01
238.200607
3
239.57
2017-03-02
236.513977
4
238.61
2017-03-03
238.252991
5
238.12
2017-03-06
237.138977
6
237.71
2017-03-07
235.989029
7
237.64
2017-03-08
237.235672
8
237.24
2017-03-09
236.047653
9
238.02
2017-03-10
235.282043
10
237.86
2017-03-13
235.183029
11
237.24
2017-03-14
236.718567
12
239.44
2017-03-15
237.555557
13
239.20
2017-03-16
234.484604
14
237.97
2017-03-17
236.761780
15
237.36
2017-03-20
235.900726
16
237.61
2017-03-21
236.543427
17
234.61
2017-03-22
236.587799
18
235.34
2017-03-23
236.514786
19
235.04
2017-03-24
236.510010
20
233.92
2017-03-27
238.011017
21
235.81
2017-03-28
237.714203
22
235.81
2017-03-29
237.909973
23
236.52
2017-03-30
236.461090
24
236.51
2017-03-31
237.961746
25
236.03
2017-04-03
237.754578
26
235.58
2017-04-04
234.507462
27
237.39
2017-04-05
235.964752
28
236.04
2017-04-06
238.192520
29
236.00
2017-04-07
237.197021
30
236.26
2017-04-10
237.994797
31
235.18
2017-04-11
234.291840
32
234.96
2017-04-12
234.716782
33
234.49
2017-04-13
238.371140
34
234.49
2017-04-14
234.810455
35
234.57
2017-04-17
238.171463
36
234.49
2017-04-18
236.883545
37
234.95
2017-04-19
235.012711
38
235.31
2017-04-21
234.795029
39
237.41
2017-04-24
236.749542
40
238.95
2017-04-25
234.834839
41
239.53
2017-04-26
236.515854
42
238.95
2017-04-27
237.048889
43
238.93
2017-04-28
236.079636
44
239.17
2017-05-01
237.461151
--------------------------------------------------
Column=FOpen_25 accuracy=0.99848460541 mse=1.73816066913 num_predictions=35
COpen
Date
FOpen
0
236.64
2017-02-27
237.816574
1
236.67
2017-02-28
235.636246
2
238.39
2017-03-01
238.271469
3
239.56
2017-03-02
236.959274
4
238.17
2017-03-03
237.465118
5
237.50
2017-03-06
237.865158
6
237.71
2017-03-07
236.993179
7
237.34
2017-03-08
234.584763
8
236.70
2017-03-09
233.244110
9
237.97
2017-03-10
237.703644
10
237.62
2017-03-13
234.808472
11
237.18
2017-03-14
235.239822
12
237.56
2017-03-15
234.950027
13
239.11
2017-03-16
235.803452
14
237.75
2017-03-17
238.045044
15
237.03
2017-03-20
236.011124
16
237.47
2017-03-21
238.083694
17
233.77
2017-03-22
235.486710
18
234.00
2017-03-23
234.097260
19
234.38
2017-03-24
235.818817
20
231.93
2017-03-27
237.592194
21
233.27
2017-03-28
234.536087
22
234.99
2017-03-29
236.317245
23
235.47
2017-03-30
235.314636
24
235.90
2017-03-31
237.147217
25
235.80
2017-04-03
236.712540
26
234.95
2017-04-04
237.701721
27
236.26
2017-04-05
234.547760
28
235.15
2017-04-07
237.012497
29
235.36
2017-04-10
237.901123
30
234.90
2017-04-11
237.447983
31
234.74
2017-04-12
235.423920
32
233.64
2017-04-13
237.207001
33
233.64
2017-04-14
234.435822
34
233.11
2017-04-17
235.358307
--------------------------------------------------
Column=FHigh_30 accuracy=0.998540184532 mse=1.89523682882 num_predictions=30
CHigh
Date
FHigh
0
237.31
2017-02-27
238.654388
1
236.95
2017-02-28
237.913757
2
240.32
2017-03-01
237.615326
3
239.57
2017-03-02
237.942841
4
238.61
2017-03-03
235.067856
5
238.12
2017-03-06
236.428833
6
237.64
2017-03-08
237.182312
7
237.24
2017-03-09
237.329041
8
238.02
2017-03-10
237.844208
9
237.86
2017-03-13
235.093140
10
237.24
2017-03-14
234.985718
11
239.44
2017-03-15
238.100098
12
239.20
2017-03-16
234.762924
13
237.97
2017-03-17
237.206512
14
237.36
2017-03-20
237.845367
15
237.61
2017-03-21
237.746216
16
234.61
2017-03-22
235.123657
17
235.34
2017-03-23
233.172958
18
235.04
2017-03-24
238.084290
19
233.92
2017-03-27
234.328537
20
235.81
2017-03-28
238.564850
21
235.81
2017-03-29
238.536072
22
236.52
2017-03-30
237.857056
23
236.51
2017-03-31
236.691391
24
236.03
2017-04-03
238.618271
25
235.58
2017-04-04
235.602158
26
237.39
2017-04-05
236.506805
27
236.04
2017-04-06
237.739807
28
236.00
2017-04-07
237.714905
29
236.26
2017-04-10
238.364258
--------------------------------------------------
Column=FOpen_20 accuracy=0.99825314721 mse=1.28484845039 num_predictions=40
COpen
Date
FOpen
0
236.64
2017-02-27
237.917526
1
236.67
2017-02-28
232.084137
2
238.39
2017-03-01
234.533905
3
239.56
2017-03-02
238.029938
4
238.17
2017-03-03
235.261230
5
237.50
2017-03-06
236.595505
6
237.71
2017-03-07
235.027740
7
237.34
2017-03-08
235.152695
8
236.70
2017-03-09
237.827057
9
237.97
2017-03-10
235.309494
10
237.62
2017-03-13
237.443634
11
237.18
2017-03-14
232.899292
12
237.56
2017-03-15
233.487885
13
239.11
2017-03-16
234.831863
14
237.75
2017-03-17
233.841690
15
237.03
2017-03-20
238.318741
16
237.47
2017-03-21
232.302078
17
233.77
2017-03-22
237.313400
18
234.00
2017-03-23
238.370819
19
234.38
2017-03-24
231.074432
20
231.93
2017-03-27
238.103638
21
233.27
2017-03-28
235.634750
22
234.99
2017-03-29
235.533920
23
235.47
2017-03-30
234.074921
24
235.90
2017-03-31
238.692703
25
235.80
2017-04-03
237.288986
26
234.95
2017-04-04
235.055527
27
236.26
2017-04-05
237.628601
28
234.94
2017-04-06
236.558319
29
235.15
2017-04-07
234.751251
30
235.36
2017-04-10
237.804810
31
234.90
2017-04-11
238.187592
32
234.74
2017-04-12
237.725464
33
233.64
2017-04-13
235.753250
34
233.64
2017-04-14
238.004852
35
233.11
2017-04-17
235.053726
36
233.72
2017-04-18
236.913940
37
234.52
2017-04-19
235.347778
38
235.25
2017-04-21
234.452515
39
237.18
2017-04-24
238.080124
--------------------------------------------------
Column=FVolume_10 accuracy=0.907178016782 mse=1.1376357129e+14 num_predictions=50
CVolume
Date
FVolume
0
56515440.0
2017-02-27
49050020.0
1
96961938.0
2017-02-28
67179288.0
2
149158170.0
2017-03-01
57029576.0
3
70245978.0
2017-03-02
74554640.0
4
81974300.0
2017-03-03
59417156.0
5
55391533.0
2017-03-06
70559072.0
6
393822.0
2017-03-07
95975664.0
7
78168795.0
2017-03-08
75058032.0
8
90683918.0
2017-03-09
80309808.0
9
81991652.0
2017-03-10
117453024.0
10
57256824.0
2017-03-13
70703744.0
11
59880778.0
2017-03-14
98328504.0
12
96081750.0
2017-03-15
109490584.0
13
78343951.0
2017-03-16
165140944.0
14
89002111.0
2017-03-17
166284832.0
15
52536979.0
2017-03-20
84392992.0
16
131809275.0
2017-03-21
68577128.0
17
97569204.0
2017-03-22
65943088.0
18
100410277.0
2017-03-23
78339784.0
19
112504853.0
2017-03-24
109490584.0
20
87454452.0
2017-03-27
93902768.0
21
93483915.0
2017-03-28
106212224.0
22
61950354.0
2017-03-29
67807328.0
23
56737890.0
2017-03-30
95485984.0
24
73733094.0
2017-03-31
51628672.0
25
85546486.0
2017-04-03
72781400.0
26
56466195.0
2017-04-04
96616176.0
27
108800604.0
2017-04-05
65563532.0
28
69135757.0
2017-04-06
56224084.0
29
74412311.0
2017-04-07
84796976.0
30
67615302.0
2017-04-10
69178496.0
31
88045276.0
2017-04-11
55412748.0
32
81864436.0
2017-04-12
120624648.0
33
92880394.0
2017-04-13
60878416.0
34
92880394.0
2017-04-14
67484416.0
35
68405367.0
2017-04-17
86180384.0
36
83225821.0
2017-04-18
103908352.0
37
68699868.0
2017-04-19
68893144.0
38
110389847.0
2017-04-21
55616128.0
39
119209877.0
2017-04-24
111632960.0
40
76698265.0
2017-04-25
64758252.0
41
84702455.0
2017-04-26
93897600.0
42
57410326.0
2017-04-27
77759200.0
43
63532845.0
2017-04-28
69928560.0
44
66882521.0
2017-05-01
129059168.0
45
57375732.0
2017-05-02
72307144.0
46
73137731.0
2017-05-03
86471680.0
47
61462732.0
2017-05-04
65864824.0
48
62001269.0
2017-05-05
77673384.0
49
48385730.0
2017-05-08
95289768.0
--------------------------------------------------
Column=FVolume_30 accuracy=0.930475108578 mse=1.29142162391e+14 num_predictions=30
CVolume
Date
FVolume
0
56515440.0
2017-02-27
154272176.0
1
96961938.0
2017-02-28
66343696.0
2
149158170.0
2017-03-01
45580672.0
3
70245978.0
2017-03-02
76765984.0
4
81974300.0
2017-03-03
74599040.0
5
55391533.0
2017-03-06
87300176.0
6
78168795.0
2017-03-08
95490720.0
7
90683918.0
2017-03-09
54597272.0
8
81991652.0
2017-03-10
70504896.0
9
57256824.0
2017-03-13
61960604.0
10
59880778.0
2017-03-14
58309852.0
11
96081750.0
2017-03-15
62352844.0
12
78343951.0
2017-03-16
59600176.0
13
89002111.0
2017-03-17
57883096.0
14
52536979.0
2017-03-20
74338800.0
15
131809275.0
2017-03-21
52386428.0
16
97569204.0
2017-03-22
73343280.0
17
100410277.0
2017-03-23
91719456.0
18
112504853.0
2017-03-24
60750428.0
19
87454452.0
2017-03-27
84663232.0
20
93483915.0
2017-03-28
63925408.0
21
61950354.0
2017-03-29
45640840.0
22
56737890.0
2017-03-30
82197232.0
23
73733094.0
2017-03-31
104147008.0
24
85546486.0
2017-04-03
57139560.0
25
56466195.0
2017-04-04
63300152.0
26
108800604.0
2017-04-05
111594736.0
27
69135757.0
2017-04-06
60577204.0
28
74412311.0
2017-04-07
87336096.0
29
67615302.0
2017-04-10
65963908.0
--------------------------------------------------
Column=FVolume_15 accuracy=0.881865035722 mse=1.50714346281e+14 num_predictions=45
CVolume
Date
FVolume
0
56515440.0
2017-02-27
58376660.0
1
96961938.0
2017-02-28
114653016.0
2
149158170.0
2017-03-01
68996536.0
3
70245978.0
2017-03-02
98230624.0
4
81974300.0
2017-03-03
53393920.0
5
55391533.0
2017-03-06
49446616.0
6
393822.0
2017-03-07
81472504.0
7
78168795.0
2017-03-08
100004472.0
8
90683918.0
2017-03-09
57248996.0
9
81991652.0
2017-03-10
71268888.0
10
57256824.0
2017-03-13
130026288.0
11
59880778.0
2017-03-14
99685816.0
12
96081750.0
2017-03-15
70750376.0
13
78343951.0
2017-03-16
94259024.0
14
89002111.0
2017-03-17
88722328.0
15
52536979.0
2017-03-20
67925600.0
16
131809275.0
2017-03-21
95118112.0
17
97569204.0
2017-03-22
56650368.0
18
100410277.0
2017-03-23
77995000.0
19
112504853.0
2017-03-24
90608664.0
20
87454452.0
2017-03-27
51847928.0
21
93483915.0
2017-03-28
64941312.0
22
61950354.0
2017-03-29
81022208.0
23
56737890.0
2017-03-30
44544140.0
24
73733094.0
2017-03-31
61810496.0
25
85546486.0
2017-04-03
64523312.0
26
56466195.0
2017-04-04
61715992.0
27
108800604.0
2017-04-05
51757008.0
28
69135757.0
2017-04-06
61607228.0
29
74412311.0
2017-04-07
128550936.0
30
67615302.0
2017-04-10
59937572.0
31
88045276.0
2017-04-11
73310760.0
32
81864436.0
2017-04-12
99653536.0
33
92880394.0
2017-04-13
61524240.0
34
92880394.0
2017-04-14
84920632.0
35
68405367.0
2017-04-17
60422616.0
36
83225821.0
2017-04-18
66732408.0
37
68699868.0
2017-04-19
53581332.0
38
110389847.0
2017-04-21
90439880.0
39
119209877.0
2017-04-24
103021816.0
40
76698265.0
2017-04-25
72200232.0
41
84702455.0
2017-04-26
60038452.0
42
57410326.0
2017-04-27
152863840.0
43
63532845.0
2017-04-28
68542696.0
44
66882521.0
2017-05-01
51390880.0
--------------------------------------------------
Column=FLow_20 accuracy=0.998418492538 mse=1.78245389644 num_predictions=40
CLow
Date
FLow
0
236.35
2017-02-27
236.516693
1
236.02
2017-02-28
232.719360
2
238.37
2017-03-01
234.400543
3
238.21
2017-03-02
236.472946
4
237.73
2017-03-03
235.309799
5
237.01
2017-03-06
235.155243
6
237.71
2017-03-07
234.749527
7
236.40
2017-03-08
234.886383
8
235.74
2017-03-09
237.387650
9
236.59
2017-03-10
234.101929
10
237.24
2017-03-13
236.617798
11
236.19
2017-03-14
234.345016
12
237.29
2017-03-15
233.537094
13
238.10
2017-03-16
234.894653
14
237.03
2017-03-17
233.620422
15
236.32
2017-03-20
236.831604
16
233.58
2017-03-21
233.621658
17
233.05
2017-03-22
237.020462
18
233.60
2017-03-23
237.310089
19
232.96
2017-03-24
230.665543
20
231.61
2017-03-27
236.240112
21
233.14
2017-03-28
235.566818
22
234.72
2017-03-29
234.099472
23
235.27
2017-03-30
233.678497
24
235.68
2017-03-31
236.371811
25
233.91
2017-04-03
235.914780
26
234.56
2017-04-04
234.261368
27
234.54
2017-04-05
236.242126
28
234.42
2017-04-06
236.769028
29
234.64
2017-04-07
235.570099
30
234.73
2017-04-10
237.313629
31
233.34
2017-04-11
236.876953
32
233.77
2017-04-12
237.146347
33
232.51
2017-04-13
235.157242
34
232.51
2017-04-14
236.425552
35
232.88
2017-04-17
233.392120
36
233.08
2017-04-18
235.693695
37
233.18
2017-04-19
235.397476
38
234.13
2017-04-21
232.840240
39
234.56
2017-04-24
236.576019
--------------------------------------------------
Column=FLow_25 accuracy=0.998601484905 mse=1.80604115137 num_predictions=35
CLow
Date
FLow
0
236.35
2017-02-27
236.538483
1
236.02
2017-02-28
233.490814
2
238.37
2017-03-01
236.478592
3
238.21
2017-03-02
237.355896
4
237.73
2017-03-03
236.198212
5
237.01
2017-03-06
237.066391
6
237.71
2017-03-07
236.528244
7
236.40
2017-03-08
233.600281
8
235.74
2017-03-09
232.905045
9
236.59
2017-03-10
236.264847
10
237.24
2017-03-13
234.175720
11
236.19
2017-03-14
235.140533
12
237.29
2017-03-15
234.296143
13
238.10
2017-03-16
234.653091
14
237.03
2017-03-17
236.870285
15
236.32
2017-03-20
236.012268
16
233.58
2017-03-21
235.739243
17
233.05
2017-03-22
234.380859
18
233.60
2017-03-23
233.576416
19
232.96
2017-03-24
234.317596
20
231.61
2017-03-27
236.920029
21
233.14
2017-03-28
236.372406
22
234.72
2017-03-29
235.406082
23
235.27
2017-03-30
235.085190
24
235.68
2017-03-31
236.167923
25
233.91
2017-04-03
237.649734
26
234.56
2017-04-04
236.838287
27
234.54
2017-04-05
233.396332
28
234.64
2017-04-07
236.977158
29
234.73
2017-04-10
236.686829
30
233.34
2017-04-11
236.656601
31
233.77
2017-04-12
234.104111
32
232.51
2017-04-13
237.367371
33
232.51
2017-04-14
233.513687
34
232.88
2017-04-17
234.464111
--------------------------------------------------
Column=FClose_30 accuracy=0.998366330784 mse=1.4605830873 num_predictions=30
CClose
Date
FClose
0
237.11
2017-02-27
236.038269
1
236.47
2017-02-28
238.432297
2
239.78
2017-03-01
238.986664
3
238.27
2017-03-02
237.920959
4
238.42
2017-03-03
234.579483
5
237.71
2017-03-06
236.220596
6
236.56
2017-03-08
235.853409
7
236.86
2017-03-09
238.039886
8
237.69
2017-03-10
237.464264
9
237.81
2017-03-13
233.276535
10
236.90
2017-03-14
233.017899
11
238.95
2017-03-15
237.794891
12
238.48
2017-03-16
233.586624
13
237.03
2017-03-17
236.831421
14
236.77
2017-03-20
237.214905
15
233.73
2017-03-21
238.634979
16
234.28
2017-03-22
234.244125
17
234.03
2017-03-23
233.014969
18
233.86
2017-03-24
238.156143
19
233.62
2017-03-27
231.728714
20
235.32
2017-03-28
237.859406
21
235.54
2017-03-29
238.588425
22
236.29
2017-03-30
237.473816
23
235.74
2017-03-31
237.031601
24
235.33
2017-04-03
237.774536
25
235.48
2017-04-04
233.693634
26
234.78
2017-04-05
235.902466
27
235.44
2017-04-06
236.567245
28
235.20
2017-04-07
236.367233
29
235.34
2017-04-10
238.269165
--------------------------------------------------
Column=FClose_15 accuracy=0.998329081518 mse=1.55153303883 num_predictions=45
CClose
Date
FClose
0
237.11
2017-02-27
237.514938
1
236.47
2017-02-28
236.349167
2
239.78
2017-03-01
237.232834
3
238.27
2017-03-02
235.958359
4
238.42
2017-03-03
237.573029
5
237.71
2017-03-06
235.776993
6
237.71
2017-03-07
233.257751
7
236.56
2017-03-08
234.547958
8
236.86
2017-03-09
236.204132
9
237.69
2017-03-10
234.440323
10
237.81
2017-03-13
235.245529
11
236.90
2017-03-14
235.861816
12
238.95
2017-03-15
237.665039
13
238.48
2017-03-16
234.893433
14
237.03
2017-03-17
235.102051
15
236.77
2017-03-20
234.524231
16
233.73
2017-03-21
235.744446
17
234.28
2017-03-22
236.093353
18
234.03
2017-03-23
235.905411
19
233.86
2017-03-24
234.976913
20
233.62
2017-03-27
238.391312
21
235.32
2017-03-28
238.297791
22
235.54
2017-03-29
237.847977
23
236.29
2017-03-30
235.961761
24
235.74
2017-03-31
237.490707
25
235.33
2017-04-03
236.914932
26
235.48
2017-04-04
234.191833
27
234.78
2017-04-05
235.793625
28
235.44
2017-04-06
238.008530
29
235.20
2017-04-07
235.931290
30
235.34
2017-04-10
237.555237
31
235.06
2017-04-11
234.810471
32
234.03
2017-04-12
233.186142
33
232.51
2017-04-13
238.099289
34
234.03
2017-04-14
234.776199
35
234.57
2017-04-17
238.332001
36
233.87
2017-04-18
235.706833
37
233.44
2017-04-19
235.013458
38
234.59
2017-04-21
234.846542
39
237.17
2017-04-24
235.933777
40
238.55
2017-04-25
234.720352
41
238.40
2017-04-26
235.560608
42
238.60
2017-04-27
235.922440
43
238.08
2017-04-28
235.809753
44
238.68
2017-05-01
237.397751
--------------------------------------------------
Column=FClose_10 accuracy=0.998336184677 mse=3.67349565905 num_predictions=50
CClose
Date
FClose
0
237.11
2017-02-27
236.719559
1
236.47
2017-02-28
235.765656
2
239.78
2017-03-01
236.341888
3
238.27
2017-03-02
234.455933
4
238.42
2017-03-03
234.006851
5
237.71
2017-03-06
239.427414
6
237.71
2017-03-07
237.065964
7
236.56
2017-03-08
238.872650
8
236.86
2017-03-09
232.399292
9
237.69
2017-03-10
238.442032
10
237.81
2017-03-13
235.934296
11
236.90
2017-03-14
234.442276
12
238.95
2017-03-15
232.743958
13
238.48
2017-03-16
232.663513
14
237.03
2017-03-17
236.680634
15
236.77
2017-03-20
236.893036
16
233.73
2017-03-21
238.207443
17
234.28
2017-03-22
238.872650
18
234.03
2017-03-23
235.746964
19
233.86
2017-03-24
235.897629
20
233.62
2017-03-27
233.996124
21
235.32
2017-03-28
233.275299
22
235.54
2017-03-29
236.156982
23
236.29
2017-03-30
235.352722
24
235.74
2017-03-31
236.800934
25
235.33
2017-04-03
234.667816
26
235.48
2017-04-04
234.142960
27
234.78
2017-04-05
236.400406
28
235.44
2017-04-06
236.916565
29
235.20
2017-04-07
236.253784
30
235.34
2017-04-10
238.985504
31
235.06
2017-04-11
237.273529
32
234.03
2017-04-12
235.987198
33
232.51
2017-04-13
238.770630
34
234.03
2017-04-14
236.319412
35
234.57
2017-04-17
235.258270
36
233.87
2017-04-18
236.158569
37
233.44
2017-04-19
238.818512
38
234.59
2017-04-21
240.200760
39
237.17
2017-04-24
232.249298
40
238.55
2017-04-25
236.471756
41
238.40
2017-04-26
232.417969
42
238.60
2017-04-27
235.982269
43
238.08
2017-04-28
235.568817
44
238.68
2017-05-01
233.334167
45
238.77
2017-05-02
235.452698
46
238.48
2017-05-03
233.297287
47
238.76
2017-05-04
236.348892
48
239.70
2017-05-05
235.108383
49
239.66
2017-05-08
235.850815
--------------------------------------------------
Column=FHigh_5 accuracy=0.998197941367 mse=1.80702890975 num_predictions=55
CHigh
Date
FHigh
0
237.31
2017-02-27
237.312714
1
236.95
2017-02-28
236.647583
2
240.32
2017-03-01
235.408691
3
239.57
2017-03-02
235.412170
4
238.61
2017-03-03
236.592255
5
238.12
2017-03-06
236.942337
6
237.71
2017-03-07
237.805847
7
237.64
2017-03-08
235.711090
8
237.24
2017-03-09
238.066299
9
238.02
2017-03-10
234.807449
10
237.86
2017-03-13
236.510498
11
237.24
2017-03-14
236.937378
12
239.44
2017-03-15
235.610382
13
239.20
2017-03-16
235.125275
14
237.97
2017-03-17
237.789337
15
237.36
2017-03-20
235.500015
16
237.61
2017-03-21
236.543228
17
234.61
2017-03-22
238.143082
18
235.34
2017-03-23
237.400833
19
235.04
2017-03-24
234.776672
20
233.92
2017-03-27
238.981216
21
235.81
2017-03-28
237.997787
22
235.81
2017-03-29
237.007416
23
236.52
2017-03-30
234.874252
24
236.51
2017-03-31
236.905701
25
236.03
2017-04-03
236.064346
26
235.58
2017-04-04
235.724167
27
237.39
2017-04-05
235.085098
28
236.04
2017-04-06
237.424881
29
236.00
2017-04-07
237.308105
30
236.26
2017-04-10
236.749710
31
235.18
2017-04-11
235.989014
32
234.96
2017-04-12
235.574341
33
234.49
2017-04-13
237.225113
34
234.49
2017-04-14
236.837738
35
234.57
2017-04-17
234.830063
36
234.49
2017-04-18
237.252274
37
234.95
2017-04-19
237.460571
38
235.31
2017-04-21
236.181732
39
237.41
2017-04-24
238.032349
40
238.95
2017-04-25
237.591309
41
239.53
2017-04-26
238.456604
42
238.95
2017-04-27
238.121109
43
238.93
2017-04-28
235.849518
44
239.17
2017-05-01
238.451675
45
238.98
2017-05-02
237.717178
46
238.88
2017-05-03
237.120483
47
238.92
2017-05-04
236.130096
48
239.72
2017-05-05
236.190536
49
239.92
2017-05-08
235.578979
50
240.19
2017-05-09
236.762985
51
240.19
2017-05-10
237.850525
52
239.46
2017-05-11
235.434219
53
239.43
2017-05-12
235.138306
54
240.44
2017-05-15
236.792328
--------------------------------------------------
Column=FClose_5 accuracy=0.998150179646 mse=1.98280524804 num_predictions=55
CClose
Date
FClose
0
237.11
2017-02-27
236.434555
1
236.47
2017-02-28
236.510025
2
239.78
2017-03-01
234.041580
3
238.27
2017-03-02
232.594925
4
238.42
2017-03-03
235.686096
5
237.71
2017-03-06
236.494537
6
237.71
2017-03-07
235.777527
7
236.56
2017-03-08
234.728180
8
236.86
2017-03-09
237.905029
9
237.69
2017-03-10
231.179260
10
237.81
2017-03-13
236.145966
11
236.90
2017-03-14
235.847733
12
238.95
2017-03-15
234.502991
13
238.48
2017-03-16
234.118912
14
237.03
2017-03-17
237.207382
15
236.77
2017-03-20
234.724274
16
233.73
2017-03-21
236.441284
17
234.28
2017-03-22
237.680954
18
234.03
2017-03-23
236.381516
19
233.86
2017-03-24
235.307632
20
233.62
2017-03-27
237.970642
21
235.32
2017-03-28
237.230652
22
235.54
2017-03-29
236.871536
23
236.29
2017-03-30
232.475800
24
235.74
2017-03-31
237.286194
25
235.33
2017-04-03
235.197113
26
235.48
2017-04-04
235.476990
27
234.78
2017-04-05
233.553894
28
235.44
2017-04-06
236.822678
29
235.20
2017-04-07
238.217285
30
235.34
2017-04-10
236.220367
31
235.06
2017-04-11
234.675919
32
234.03
2017-04-12
233.628799
33
232.51
2017-04-13
237.408386
34
234.03
2017-04-14
237.107529
35
234.57
2017-04-17
233.886322
36
233.87
2017-04-18
237.400650
37
233.44
2017-04-19
237.057693
38
234.59
2017-04-21
234.789215
39
237.17
2017-04-24
234.993515
40
238.55
2017-04-25
236.682175
41
238.40
2017-04-26
237.013947
42
238.60
2017-04-27
236.689621
43
238.08
2017-04-28
235.828934
44
238.68
2017-05-01
236.930801
45
238.77
2017-05-02
236.634476
46
238.48
2017-05-03
235.985107
47
238.76
2017-05-04
235.990326
48
239.70
2017-05-05
236.880493
49
239.66
2017-05-08
234.437592
50
239.44
2017-05-09
237.104736
51
239.66
2017-05-10
237.244461
52
239.87
2017-05-11
233.662079
53
238.98
2017-05-12
233.010788
54
240.30
2017-05-15
236.858719
In [6]:
job_res = get_job_analysis(job_id, show_plots=True)
Getting analysis for job=558 url=https://redten.io/ml/analysis/558/
SUCCESS - GET Analysis Response Status=200 Reason=OK
Found Job=558 analysis
SPY-2-558 5-Days - Predictive Accuracy
Predicted Close 5 Days vs Actual Close 5 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12122_7c49dccd964548fc.png
---------------------------------------------------------------------------------------
SPY-2-558 5-Days - Predictive Accuracy
Predicted High 5 Days vs Actual High 5 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12121_5ae84fdafa034174.png
---------------------------------------------------------------------------------------
SPY-2-558 10-Days - Predictive Accuracy
Predicted Close 10 Days vs Actual Close 10 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12120_2d87f32772344921.png
---------------------------------------------------------------------------------------
SPY-2-558 15-Days - Predictive Accuracy
Predicted Close 15 Days vs Actual Close 15 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12119_c54a1003037d4eb3.png
---------------------------------------------------------------------------------------
SPY-2-558 30-Days - Predictive Accuracy
Predicted Close 30 Days vs Actual Close 30 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12118_192e7a3c4ba840fc.png
---------------------------------------------------------------------------------------
SPY-2-558 25-Days - Predictive Accuracy
Predicted Low 25 Days vs Actual Low 25 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12117_6b74cc14075f4f20.png
---------------------------------------------------------------------------------------
SPY-2-558 20-Days - Predictive Accuracy
Predicted Low 20 Days vs Actual Low 20 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12116_1fed112e3a4e42ff.png
---------------------------------------------------------------------------------------
SPY-2-558 15-Days - Predictive Accuracy
Predicted Volume 15 Days vs Actual Volume 15 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12115_795d9657c2534f76.png
---------------------------------------------------------------------------------------
SPY-2-558 30-Days - Predictive Accuracy
Predicted Volume 30 Days vs Actual Volume 30 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12114_c19203024ec84944.png
---------------------------------------------------------------------------------------
SPY-2-558 10-Days - Predictive Accuracy
Predicted Volume 10 Days vs Actual Volume 10 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12113_1b000088cf154451.png
---------------------------------------------------------------------------------------
SPY-2-558 20-Days - Predictive Accuracy
Predicted Open 20 Days vs Actual Open 20 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12112_c72e079979774d17.png
---------------------------------------------------------------------------------------
SPY-2-558 30-Days - Predictive Accuracy
Predicted High 30 Days vs Actual High 30 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12111_b0c21c4c4d0d4e7a.png
---------------------------------------------------------------------------------------
SPY-2-558 25-Days - Predictive Accuracy
Predicted Open 25 Days vs Actual Open 25 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12110_2a27883839464a6c.png
---------------------------------------------------------------------------------------
SPY-2-558 15-Days - Predictive Accuracy
Predicted High 15 Days vs Actual High 15 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12109_325e5fefa07a444d.png
---------------------------------------------------------------------------------------
SPY-2-558 10-Days - Predictive Accuracy
Predicted High 10 Days vs Actual High 10 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12108_7dfe5cad6dd341c2.png
---------------------------------------------------------------------------------------
SPY-2-558 15-Days - Predictive Accuracy
Predicted Low 15 Days vs Actual Low 15 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12107_2fcaa612a10346d9.png
---------------------------------------------------------------------------------------
SPY-2-558 5-Days - Predictive Accuracy
Predicted Open 5 Days vs Actual Open 5 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12106_29c5e186270348f4.png
---------------------------------------------------------------------------------------
SPY-2-558 10-Days - Predictive Accuracy
Predicted Low 10 Days vs Actual Low 10 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12105_7139a2dd4df5447b.png
---------------------------------------------------------------------------------------
SPY-2-558 25-Days - Predictive Accuracy
Predicted Close 25 Days vs Actual Close 25 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12104_74d4b72e2bd342d6.png
---------------------------------------------------------------------------------------
SPY-2-558 5-Days - Predictive Accuracy
Predicted Volume 5 Days vs Actual Volume 5 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12103_286431b3eabc4af5.png
---------------------------------------------------------------------------------------
SPY-2-558 20-Days - Predictive Accuracy
Predicted Close 20 Days vs Actual Close 20 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12102_b8f6174000f5455c.png
---------------------------------------------------------------------------------------
SPY-2-558 30-Days - Predictive Accuracy
Predicted Low 30 Days vs Actual Low 30 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12101_bfe9888ad15648f9.png
---------------------------------------------------------------------------------------
SPY-2-558 25-Days - Predictive Accuracy
Predicted Volume 25 Days vs Actual Volume 25 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12100_0761ea1b0f684ce9.png
---------------------------------------------------------------------------------------
SPY-2-558 20-Days - Predictive Accuracy
Predicted Volume 20 Days vs Actual Volume 20 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12099_8f73f41290794595.png
---------------------------------------------------------------------------------------
SPY-2-558 20-Days - Predictive Accuracy
Predicted High 20 Days vs Actual High 20 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12098_5fd3e28826754f24.png
---------------------------------------------------------------------------------------
SPY-2-558 30-Days - Predictive Accuracy
Predicted Open 30 Days vs Actual Open 30 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12097_214858c79e704f4c.png
---------------------------------------------------------------------------------------
SPY-2-558 25-Days - Predictive Accuracy
Predicted High 25 Days vs Actual High 25 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12096_168bcf41ff5142ad.png
---------------------------------------------------------------------------------------
SPY-2-558 15-Days - Predictive Accuracy
Predicted Open 15 Days vs Actual Open 15 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12095_de06fd5548504b41.png
---------------------------------------------------------------------------------------
SPY-2-558 5-Days - Predictive Accuracy
Predicted Low 5 Days vs Actual Low 5 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12094_354bb5267c694ba5.png
---------------------------------------------------------------------------------------
SPY-2-558 10-Days - Predictive Accuracy
Predicted Open 10 Days vs Actual Open 10 Days
URL: https://rt-media.s3.amazonaws.com/media/imagesml/20170526/2_558_12093_e2ecdc03bc8a4f30.png
In [7]:
user_token = user_login(rt_user, rt_pass, rt_url)
auth_headers = {
"Authorization" : "JWT " + str(user_token)
}
resource_url = rt_url + "/ml/run/"
query_params = {}
post_data = {}
# Get the ML Job
resource_url = rt_url + "/ml/jobs/"
lg("Running Get ML Job url=" + str(resource_url), 6)
get_response = requests.get(resource_url, params=query_params, data=post_data, headers=auth_headers)
if get_response.status_code != 201 and get_response.status_code != 200:
lg("Failed with GET Response Status=" + str(get_response.status_code) + " Reason=" + str(get_response.reason), 0)
lg("Details:\n" + str(get_response.text) + "\n", 0)
else:
lg("SUCCESS - GET Response Status=" + str(get_response.status_code) + " Reason=" + str(get_response.reason)[0:10], 5)
as_json = True
record = {}
if as_json:
record = json.loads(get_response.text)
lg(ppj(record))
# end of post for running an ML Job
Running Get ML Job url=https://redten.io/ml/jobs/
SUCCESS - GET Response Status=200 Reason=OK
{
"jobs": [
{
"algo_name": "xgb-regressor",
"control_state": "active",
"created": "2017-05-26 08-02-13",
"csv_file": "",
"desc": "Forecast simulation - 2017-05-26 08:02:13",
"ds_name": "SPY",
"feature_column_names": [
"FHigh",
"FLow",
"FOpen",
"FClose",
"FVolume"
],
"id": 558,
"ignore_features": [
"Ticker",
"Date",
"FDate",
"FPrice",
"DcsnDate",
"Decision"
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"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11985_da5aa1505dc44776.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11984_8980ae40b6d944b5.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11983_6c550ae6bc9c43ff.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11982_a3fbf30ee01144f6.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11981_c5c10073f6e04e0d.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11980_606d78feecf84b27.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11979_5b6dc14929654885.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11978_f2e3475eb2ff48d4.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11977_ff15925c6cd3433c.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11976_2117953502474673.png",
"https://rt-media.s3.amazonaws.com/media/imagesml/20170523/2_554_11975_723d049fdaf64c5b.png"
],
"manifest_secret_key": "live_2_c0906bd2b8dc438eaf3c06415ccbf5705b4b2e10f744b9abcf036377d81b62",
"manifest_sloc": "redten-models-west:SPY-2-554_manifest.json",
"max_features": 10,
"ml_type": "Playbook-UnitsAhead",
"prediction_type": "forecast",
"rloc": "",
"sloc": "",
"status": "completed",
"target_column_name": "FClose",
"target_column_values": [
"GoodBuys",
"BadBuys",
"Not Finished"
],
"title": "SPY Forecast v5 - 739024c2c4f745c89c978040405bf22",
"units_ahead_set": [
5,
10,
15,
20,
25,
30
],
"units_ahead_type": "Days",
"updated": "2017-05-23 18-01-41",
"version": 1
}
]
}
Jobs use a manifest to prevent concurrent jobs in-flight and models from colliding between users and historical machine learning jobs
In [8]:
job_manifest = get_job_cache_manifest(job_report)
lg(ppj(job_manifest))
{
"11811": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_0",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11811_modelkey_MD_SPY-2-558_cd1d9a_0.cache.pickle.zlib",
"target": "FOpen_10",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11812": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_1",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11812_modelkey_MD_SPY-2-558_cd1d9a_1.cache.pickle.zlib",
"target": "FLow_5",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11813": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_2",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11813_modelkey_MD_SPY-2-558_cd1d9a_2.cache.pickle.zlib",
"target": "FOpen_15",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11814": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_3",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11814_modelkey_MD_SPY-2-558_cd1d9a_3.cache.pickle.zlib",
"target": "FHigh_25",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11815": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_4",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11815_modelkey_MD_SPY-2-558_cd1d9a_4.cache.pickle.zlib",
"target": "FOpen_30",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11816": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_5",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11816_modelkey_MD_SPY-2-558_cd1d9a_5.cache.pickle.zlib",
"target": "FHigh_20",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11817": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_6",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11817_modelkey_MD_SPY-2-558_cd1d9a_6.cache.pickle.zlib",
"target": "FVolume_20",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11818": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_7",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11818_modelkey_MD_SPY-2-558_cd1d9a_7.cache.pickle.zlib",
"target": "FVolume_25",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11819": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_8",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11819_modelkey_MD_SPY-2-558_cd1d9a_8.cache.pickle.zlib",
"target": "FLow_30",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11820": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_9",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11820_modelkey_MD_SPY-2-558_cd1d9a_9.cache.pickle.zlib",
"target": "FClose_20",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11821": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_10",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11821_modelkey_MD_SPY-2-558_cd1d9a_10.cache.pickle.zlib",
"target": "FVolume_5",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11822": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_11",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11822_modelkey_MD_SPY-2-558_cd1d9a_11.cache.pickle.zlib",
"target": "FClose_25",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11823": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_12",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11823_modelkey_MD_SPY-2-558_cd1d9a_12.cache.pickle.zlib",
"target": "FLow_10",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11824": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_13",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11824_modelkey_MD_SPY-2-558_cd1d9a_13.cache.pickle.zlib",
"target": "FOpen_5",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11825": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_14",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11825_modelkey_MD_SPY-2-558_cd1d9a_14.cache.pickle.zlib",
"target": "FLow_15",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11826": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_15",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11826_modelkey_MD_SPY-2-558_cd1d9a_15.cache.pickle.zlib",
"target": "FHigh_10",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11827": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_16",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11827_modelkey_MD_SPY-2-558_cd1d9a_16.cache.pickle.zlib",
"target": "FHigh_15",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11828": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_17",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11828_modelkey_MD_SPY-2-558_cd1d9a_17.cache.pickle.zlib",
"target": "FOpen_25",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11829": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_18",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11829_modelkey_MD_SPY-2-558_cd1d9a_18.cache.pickle.zlib",
"target": "FHigh_30",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11830": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_19",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11830_modelkey_MD_SPY-2-558_cd1d9a_19.cache.pickle.zlib",
"target": "FOpen_20",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11831": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_20",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11831_modelkey_MD_SPY-2-558_cd1d9a_20.cache.pickle.zlib",
"target": "FVolume_10",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11832": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_21",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11832_modelkey_MD_SPY-2-558_cd1d9a_21.cache.pickle.zlib",
"target": "FVolume_30",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11833": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_22",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11833_modelkey_MD_SPY-2-558_cd1d9a_22.cache.pickle.zlib",
"target": "FVolume_15",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11834": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_23",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11834_modelkey_MD_SPY-2-558_cd1d9a_23.cache.pickle.zlib",
"target": "FLow_20",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11835": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_24",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11835_modelkey_MD_SPY-2-558_cd1d9a_24.cache.pickle.zlib",
"target": "FLow_25",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11836": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_25",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11836_modelkey_MD_SPY-2-558_cd1d9a_25.cache.pickle.zlib",
"target": "FClose_30",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11837": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_26",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11837_modelkey_MD_SPY-2-558_cd1d9a_26.cache.pickle.zlib",
"target": "FClose_15",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11838": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_27",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11838_modelkey_MD_SPY-2-558_cd1d9a_27.cache.pickle.zlib",
"target": "FClose_10",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11839": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_28",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11839_modelkey_MD_SPY-2-558_cd1d9a_28.cache.pickle.zlib",
"target": "FHigh_5",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
},
"11840": {
"rloc": "MODELS:_MD_SPY-2-558_cd1d9a_29",
"sloc": "redten-models-west:rt_models_userid_2_job_558_train_450_jobresults_413_modelid_11840_modelkey_MD_SPY-2-558_cd1d9a_29.cache.pickle.zlib",
"target": "FClose_5",
"tracking_id": "ML_SPY-2-558_95aa375d14684a8182fe20b566090a3",
"tracking_name": "SPY-2-558",
"tracking_type": "UseTargetColAndUnits"
}
}
Today's talk focused on:
Content source: jay-johnson/sci-pype
Similar notebooks: