Backtesting

Backtesting is the process of testing a trading strategy on historical market data to see how it would have performed under those trading conditions. Quantitative Developers and Analysts will use a market simulator (like CloudQuant) to evaluate the trading strategy. Key statistics that show performance are shown on the CloudQuant scorecard. Statistics include Sharpe Ratio, Calmar Ratio, Kelly Edge Percentages, Profit/Loss, Drawdown.

Backtesting Quantitative Algorithms on CloudQuant

Posts

CloudQuant ESG with G&S Quotient 2020 Q1

≈9% Return for Shelter at Home & ESG Investing

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Investing in ESG has been crazy in the past few months. A recent market-neutral backtest showed a 8.73% return (Sharpe of 4.98) for the period of January 1, 2020, to April 22, 2020.
Alternative Data Portfolio Returns on Intraday Hold Trading Strategy

The Value in Machine Learning Alternative Data for Investment Managers

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A long-short portfolio outperforms the equal-weight S&P 500 ETF by 37.9%/year (after transaction costs) using Precision Alpha Alternative Data. Over 91.5% of the total return is pure alpha.
bulls and bears

SMB Quant Video 1 – Developing a Model

SMB Capital recently started a new series of Videos/Podcasts where they interview Quants both inside and outside of their company. In the first video, Jeff Holden of SMB steps through his process for developing a model :
  • Data Curation – What data do I need for this model?
  • Feature Analytics – Taking informative features and turning them into actual investment algorithms
  • Strategy – Take the analysis and turn it into a Theory – an understanding of WHY it works, what is the reason or motivation that causes the other trader on the opposite side to lose money to us. Often you finds something other than what you were looking for or even the exact opposite.
  • Backtesting – Assess – Test in unusual market environments – try to break it.
  • Deployment – Testing the model with live market data, Optimization, Scaling and Portfolio selection.
  • Q&A Session
Applications used : CloudQuant, Jupyter Notebooks, Gr8Trade (KTG Group), Gr8Py (KTG Group) Duration : 51m
AdobeStock_156702375

SMB Quant Video 2 – Democratization of Trading – CloudQuant

In video two of SMB’s new Quant series they interview yours truly, Paul Tunney, to discuss what CloudQuant is and why you should use it to develop an algorithmic trading model. In this video we discuss…
  • The Goal of CQ – The democratization of Trading Data and systems.
  • Pre-Requisites – You will need to know Python, but the skill of programming will be hugely beneficial for your entire life.
  • How to create a model – Why the question “How do I create a model?” is similar to the question “How do I write a song?”
  • What is unique about CQ – Built by real world Quants for real world Quants 
  • What about ML and AI – Requires a lot of data, getting, managing and using that data is difficult.
  • What we hope people will do with CQ – We hope to get a few great new smart Algo creators who are currently outside of Wall Street.
  • Funding – We will fund your models, at a level you could never manage yourself. And we take on the risk.
  • The Future – CloudQuant AI
  Duration : 63m
Trading strategies require thought

Conversations: Recommendations to Someone Starting out at CloudQuant

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The CloudQuant team discusses their helpful thoughts for beginners on CloudQuant. We want to boost everyone starting out on our platform in their algo development and backtesting. Everyone in our company uses the CloudQuant website and coding platform in one way or another. We all use our own application, just like the crowd researchers. When we say that our free backtesting tools are “institutional grade” we really mean it. Every algo we run in our trading and investment strategies is proven in the same backtesting engine as the crowd uses. We rely on the scorecards, the reports, and the simulated trades to ensure that our trading is successful.
$QQQ ETH spreads wider than RTH spreads

US Stock Market pre-market and post-market bid-ask spreads are different than regular trading hours

Hours and Market Data on The US Stock Markets

Regular Trading Hours (RTH) in the US Stock Market is 9:30 a.m. to 4:00 p.m. Eastern Time. Trading can happen in the Pre-market hours (4:00 a.m. to 9:30 a.m. ET) and in the After Hours (post) market (4:00 p.m. to 8:00). The pre-market and post-market are sometimes called Extended Trading Hours (ETH). ETH markets do not behave the same as the RT sometimes called after-hours trading. To illustrate this here are a few graphs that show the spread over time for some of the QQQ, one of the most popular trading symbols in both RTH and ETH. The following chart clearly shows that the average bid-ask spread during each minute bar varies widely in ETH. $QQQ ETH spreads wider than RTH spreads
  • The blue highlights are the pre-market hours.
  • The yellow highlights are RTH.
  • The green highlights are the post-market hours.
  This chart was generated using CloudQuant’s free historical backtesting tool. The python historical market data is shown below.
from cloudquant.interfaces import Strategy

    class CQb5e7901ea3bc4e22b0ffd861ce3f0287(Strategy):

    # note that this doesn't start with "self" because it's a @classmethod
    @classmethod
    def is_symbol_qualified(cls, symbol, md, service, account):
        return symbol=="QQQ" 

    # called at the beginning of each instance
    def on_start(self, md, order, service, account):
        self.filename = "spread_{}.csv".format(self.symbol)
        service.write_file(self.filename, "time,symbol,spread")

    # called every minute before the first on_trade of every new minute, or 5 seconds after a new minute starts
    def on_minute_bar(self, event, md, order, service, account, bar):
        mybars = md.bar.minute(start=-1,include_extended=True)
        if len(mybars.spread) > 0:
            service.write_file(self.filename, "{},{},{}".format(service.time_to_string(event.timestamp), self.symbol, mybars.spread[-1]))
        else:
            service.write_file(self.filename, "{},{},{}".format(service.time_to_string(event.timestamp), self.symbol, 0))

Fields available in the 1 minute bar data

Bars Access to historic Formed bar data Daily and Minute. For data from the forming bars (current incomplete “live” bar) see md.L1. above
md.bars. Data Passed in This is the data you pass to the bar calls
start An offset. Normally negative -10 = 10 days/minutes ago. Or a timestamp.
end An offset. Optional. Normally negative -8 = 8 days/minutes ago. Or a timestamp.
include_empty Returns bars for empty minutes, Empty bars contain NaN if no trades happened in bar (use .valid to check bar)
include_extended MINUTE ONLY Include pre/post minute bars
bar_size MINUTE ONLY Minute bar size. Based off 9:30 start time
today_only MINUTE EXTENDED ONLY Limits the bars returned to today only
md.bars Data returned This is the data returned by the bar call
askvol the volume of trades that occurred at the ask during timeframe
avgdelta Average change in price between trades during time period.
bidvol the volume of trades that occurred at the bid during timeframe
bvwap DAILY ONLY the vwap for the bar, see md.L1.minute_vwap for the vwap for the current minute
bwap LIVE MINUTE ONLY the vwap for the minute bar but is only available on the live trading system
close final, or closing price for the bar
closing_trade DAILY ONLY closing price for the symbol’s primary exchange (6 print, MOC print)
count number of items contributing to the bar
high highest price for the bar
length time length for the bar (rolled bars – see bar_size above)
low lowest price for the bar
open first qualifying trade of the bar, so for daily bars this will be the first non odd-lot print after 9:30
opening_trade DAILY ONLYopening price for the symbol’s primary exchange (‘O’ print, MOO print)if 0.0 use .open
spread average difference between the ask and the bid for the bar
symbol symbol for the bar
timestamp timestamps for the beginning of each bar, use service.time_to_string(muts,format=”%Y-%m-%d %H:%M:%S.%f”) to pull the date/time
valid whether or not bar is valid
volume volume for the bar
vwap volume weighted average price(vwap) of the bar, for minute bars this is the vwap for the day up to this point

1 Day Spread Supporting Data

Time Symbo Spread
4:01:01 AM QQQ 0.4700
4:02:05 AM QQQ 0.8300
4:03:05 AM QQQ 0.0375
4:04:05 AM QQQ 0.0300
4:05:05 AM QQQ 0.0300
4:06:01 AM QQQ 0.0475
4:07:05 AM QQQ 0.0567
4:08:05 AM QQQ 0.0600
4:09:05 AM QQQ 0.0500
4:10:05 AM QQQ 0.0500
4:11:05 AM QQQ 0.0500
4:12:05 AM QQQ 0.0700
4:13:05 AM QQQ 0.0700
4:14:00 AM QQQ 0.0000
4:15:05 AM QQQ 0.0350
4:16:05 AM QQQ 0.0500
4:17:05 AM QQQ 0.0500
4:18:05 AM QQQ 0.0000
4:19:05 AM QQQ 0.0000
4:20:05 AM QQQ 0.0000
4:21:05 AM QQQ 0.0600
4:22:05 AM QQQ 0.0700
4:23:05 AM QQQ 0.0600
4:24:05 AM QQQ 0.0250
4:25:05 AM QQQ 0.0486
4:26:05 AM QQQ 0.0400
4:27:05 AM QQQ 0.0400
4:28:05 AM QQQ 0.0300
4:29:05 AM QQQ 0.0300
4:30:05 AM QQQ 0.0200
4:31:05 AM QQQ 0.0133
4:32:05 AM QQQ 0.0133
4:33:05 AM QQQ 0.0300
4:34:05 AM QQQ 0.0300
4:35:05 AM QQQ 0.0300
4:36:05 AM QQQ 0.0300
4:37:05 AM QQQ 0.0000
4:38:05 AM QQQ 0.0000
4:39:05 AM QQQ 0.0300
4:40:02 AM QQQ 0.0200
4:41:05 AM QQQ 0.0225
4:42:05 AM QQQ 0.0300
4:43:05 AM QQQ 0.0200
4:44:05 AM QQQ 0.0300
4:45:05 AM QQQ 0.0400
4:46:05 AM QQQ 0.0400
4:47:05 AM QQQ 0.0200
4:48:05 AM QQQ 0.0200
4:49:05 AM QQQ 0.0300
4:50:05 AM QQQ 0.0300
4:51:05 AM QQQ 0.0000
4:52:05 AM QQQ 0.0000
4:53:05 AM QQQ 0.0300
4:54:05 AM QQQ 0.0300
4:55:05 AM QQQ 0.0000
4:56:03 AM QQQ 0.0000
4:57:05 AM QQQ 0.0250
4:58:05 AM QQQ 0.0250
4:59:05 AM QQQ 0.0000
5:00:05 AM QQQ 0.0000
5:01:05 AM QQQ 0.0300
5:02:05 AM QQQ 0.0300
5:03:05 AM QQQ 0.0000
5:04:05 AM QQQ 0.0250
5:05:05 AM QQQ 0.0400
5:06:05 AM QQQ 0.0300
5:07:05 AM QQQ 0.0300
5:08:05 AM QQQ 0.0000
5:09:05 AM QQQ 0.0000
5:10:05 AM QQQ 0.0000
5:11:05 AM QQQ 0.0300
5:12:05 AM QQQ 0.0300
5:13:05 AM QQQ 0.0300
5:14:05 AM QQQ 0.0300
5:15:05 AM QQQ 0.0300
5:16:05 AM QQQ 0.0000
5:17:03 AM QQQ 0.0187
5:18:05 AM QQQ 0.0300
5:19:05 AM QQQ 0.0300
5:20:05 AM QQQ 0.0300
5:21:05 AM QQQ 0.0300
5:22:05 AM QQQ 0.0300
5:23:05 AM QQQ 0.0300
5:24:05 AM QQQ 0.0000
5:25:05 AM QQQ 0.0000
5:26:05 AM QQQ 0.0000
5:27:05 AM QQQ 0.0000
5:28:05 AM QQQ 0.0300
5:29:05 AM QQQ 0.0300
5:30:05 AM QQQ 0.0300
5:31:05 AM QQQ 0.0300
5:32:05 AM QQQ 0.0200
5:33:05 AM QQQ 0.0250
5:34:05 AM QQQ 0.0250
5:35:05 AM QQQ 0.0233
5:36:05 AM QQQ 0.0300
5:37:05 AM QQQ 0.0300
5:38:05 AM QQQ 0.0300
5:39:05 AM QQQ 0.0050
5:40:02 AM QQQ 0.0050
5:41:05 AM QQQ 0.0333
5:42:05 AM QQQ 0.0300
5:43:05 AM QQQ 0.0300
5:44:05 AM QQQ 0.0220
5:45:05 AM QQQ 0.0300
5:46:05 AM QQQ 0.0100
5:47:00 AM QQQ 0.0400
5:48:05 AM QQQ 0.0367
5:49:05 AM QQQ 0.0367
5:50:05 AM QQQ 0.0400
5:51:05 AM QQQ 0.0300
5:52:05 AM QQQ 0.0100
5:53:05 AM QQQ 0.0233
5:54:05 AM QQQ 0.0333
5:55:05 AM QQQ 0.0400
5:56:05 AM QQQ 0.0400
5:57:05 AM QQQ 0.0300
5:58:05 AM QQQ 0.0150
5:59:05 AM QQQ 0.0150
6:00:05 AM QQQ 0.0300
6:01:05 AM QQQ 0.0200
6:02:01 AM QQQ 0.0200
6:03:02 AM QQQ 0.0300
6:04:05 AM QQQ 0.0150
6:05:05 AM QQQ 0.0150
6:06:05 AM QQQ 0.0400
6:07:05 AM QQQ 0.0400
6:08:05 AM QQQ 0.0400
6:09:04 AM QQQ 0.0300
6:10:05 AM QQQ 0.0400
6:11:05 AM QQQ 0.0400
6:12:05 AM QQQ 0.0100
6:13:05 AM QQQ 0.0100
6:14:04 AM QQQ 0.0300
6:15:05 AM QQQ 0.0364
6:16:05 AM QQQ 0.0364
6:17:05 AM QQQ 0.0400
6:18:05 AM QQQ 0.0400
6:19:05 AM QQQ 0.0317
6:20:05 AM QQQ 0.0300
6:21:02 AM QQQ 0.0400
6:22:02 AM QQQ 0.0200
6:23:05 AM QQQ 0.0400
6:24:05 AM QQQ 0.0400
6:25:03 AM QQQ 0.0417
6:26:05 AM QQQ 0.0400
6:27:05 AM QQQ 0.0400
6:28:05 AM QQQ 0.0400
6:29:05 AM QQQ 0.0400
6:30:05 AM QQQ 0.0100
6:31:05 AM QQQ 0.0400
6:32:05 AM QQQ 0.0214
6:33:05 AM QQQ 0.0300
6:34:05 AM QQQ 0.0300
6:35:05 AM QQQ 0.0400
6:36:05 AM QQQ 0.0400
6:37:05 AM QQQ 0.0100
6:38:05 AM QQQ 0.0100
6:39:05 AM QQQ 0.0000
6:40:05 AM QQQ 0.0300
6:41:05 AM QQQ 0.0300
6:42:05 AM QQQ 0.0300
6:43:05 AM QQQ 0.0300
6:44:05 AM QQQ 0.0300
6:45:05 AM QQQ 0.0000
6:46:05 AM QQQ 0.0000
6:47:05 AM QQQ 0.0000
6:48:05 AM QQQ 0.0200
6:49:03 AM QQQ 0.0238
6:50:05 AM QQQ 0.0275
6:51:04 AM QQQ 0.0275
6:52:05 AM QQQ 0.0250
6:53:05 AM QQQ 0.0225
6:54:05 AM QQQ 0.0225
6:55:03 AM QQQ 0.0000
6:56:05 AM QQQ 0.0300
6:57:05 AM QQQ 0.0300
6:58:05 AM QQQ 0.0200
6:59:04 AM QQQ 0.0100
7:00:00 AM QQQ 0.0080
7:01:05 AM QQQ 0.0169
7:02:01 AM QQQ 0.0156
7:03:03 AM QQQ 0.0140
7:04:05 AM QQQ 0.0131
7:05:05 AM QQQ 0.0100
7:06:05 AM QQQ 0.0067
7:07:05 AM QQQ 0.0067
7:08:05 AM QQQ 0.0087
7:09:05 AM QQQ 0.0213
7:10:02 AM QQQ 0.0073
7:11:05 AM QQQ 0.0208
7:12:05 AM QQQ 0.0325
7:13:05 AM QQQ 0.0275
7:14:05 AM QQQ 0.0086
7:15:05 AM QQQ 0.0125
7:16:05 AM QQQ 0.0025
7:17:05 AM QQQ 0.0025
7:18:01 AM QQQ 0.0250
7:19:05 AM QQQ 0.0147
7:20:05 AM QQQ 0.0260
7:21:05 AM QQQ 0.0176
7:22:05 AM QQQ 0.0244
7:23:05 AM QQQ 0.0244
7:24:05 AM QQQ 0.0000
7:25:05 AM QQQ 0.0300
7:26:05 AM QQQ 0.0300
7:27:05 AM QQQ 0.0224
7:28:05 AM QQQ 0.0300
7:29:05 AM QQQ 0.0167
7:30:05 AM QQQ 0.0129
7:31:03 AM QQQ 0.0089
7:32:05 AM QQQ 0.0085
7:33:05 AM QQQ 0.0240
7:34:05 AM QQQ 0.0161
7:35:02 AM QQQ 0.0089
7:36:05 AM QQQ -0.0020
7:37:05 AM QQQ 0.0100
7:38:05 AM QQQ 0.0100
7:39:05 AM QQQ 0.0044
7:40:05 AM QQQ 0.0200
7:41:04 AM QQQ 0.0000
7:42:03 AM QQQ 0.0100
7:43:01 AM QQQ 0.0125
7:44:05 AM QQQ 0.0208
7:45:05 AM QQQ 0.0100
7:46:05 AM QQQ 0.0250
7:47:05 AM QQQ 0.0112
7:48:05 AM QQQ 0.0064
7:49:05 AM QQQ 0.0064
7:50:05 AM QQQ 0.0000
7:51:05 AM QQQ 0.0000
7:52:05 AM QQQ 0.0050
7:53:03 AM QQQ 0.0100
7:54:05 AM QQQ 0.0200
7:55:05 AM QQQ 0.0100
7:56:05 AM QQQ 0.0200
7:57:05 AM QQQ 0.0267
7:58:05 AM QQQ 0.0226
7:59:05 AM QQQ 0.0075
8:00:01 AM QQQ 0.0040
8:01:02 AM QQQ 0.0085
8:02:05 AM QQQ 0.0171
8:03:05 AM QQQ 0.0200
8:04:01 AM QQQ 0.0200
8:05:01 AM QQQ 0.0183
8:06:00 AM QQQ 0.0210
8:07:05 AM QQQ 0.0189
8:08:04 AM QQQ 0.0150
8:09:05 AM QQQ 0.0167
8:10:05 AM QQQ 0.0100
8:11:05 AM QQQ 0.0225
8:12:05 AM QQQ 0.0200
8:13:05 AM QQQ 0.0025
8:14:05 AM QQQ 0.0133
8:15:02 AM QQQ 0.0025
8:16:00 AM QQQ 0.0150
8:17:05 AM QQQ 0.0200
8:18:05 AM QQQ 0.0200
8:19:05 AM QQQ 0.0100
8:20:05 AM QQQ 0.0200
8:21:05 AM QQQ 0.0113
8:22:05 AM QQQ 0.0120
8:23:05 AM QQQ 0.0300
8:24:03 AM QQQ 0.0200
8:25:05 AM QQQ 0.0021
8:26:00 AM QQQ 0.0200
8:27:05 AM QQQ 0.0300
8:28:05 AM QQQ 0.0400
8:29:05 AM QQQ 0.0175
8:30:05 AM QQQ 0.0125
8:31:05 AM QQQ 0.0117
8:32:05 AM QQQ 0.0214
8:33:05 AM QQQ 0.0214
8:34:03 AM QQQ 0.0160
8:35:05 AM QQQ 0.0471
8:36:05 AM QQQ 0.0075
8:37:05 AM QQQ 0.0075
8:38:05 AM QQQ 0.0300
8:39:05 AM QQQ 0.0200
8:40:05 AM QQQ 0.0200
8:41:05 AM QQQ 0.0238
8:42:00 AM QQQ 0.0200
8:43:04 AM QQQ 0.0220
8:44:05 AM QQQ 0.0200
8:45:01 AM QQQ 0.0086
8:46:05 AM QQQ 0.0306
8:47:05 AM QQQ 0.0189
8:48:00 AM QQQ 0.0167
8:49:05 AM QQQ 0.0150
8:50:05 AM QQQ 0.0100
8:51:02 AM QQQ 0.0023
8:52:01 AM QQQ 0.0237
8:53:05 AM QQQ 0.0218
8:54:05 AM QQQ 0.0262
8:55:05 AM QQQ 0.0186
8:56:05 AM QQQ 0.0100
8:57:05 AM QQQ 0.0162
8:58:05 AM QQQ 0.0133
8:59:05 AM QQQ 0.0125
9:00:05 AM QQQ 0.0217
9:01:05 AM QQQ 0.0160
9:02:05 AM QQQ 0.0144
9:03:05 AM QQQ 0.0100
9:04:05 AM QQQ 0.0067
9:05:05 AM QQQ 0.0200
9:06:01 AM QQQ 0.0127
9:07:05 AM QQQ 0.0100
9:08:00 AM QQQ 0.0141
9:09:05 AM QQQ 0.0137
9:10:05 AM QQQ 0.0100
9:11:04 AM QQQ 0.0147
9:12:05 AM QQQ 0.0026
9:13:05 AM QQQ 0.0100
9:14:05 AM QQQ 0.0136
9:15:05 AM QQQ 0.0186
9:16:05 AM QQQ 0.0125
9:17:03 AM QQQ 0.0212
9:18:05 AM QQQ 0.0108
9:19:05 AM QQQ 0.0174
9:20:02 AM QQQ 0.0180
9:21:05 AM QQQ 0.0216
9:22:02 AM QQQ 0.0162
9:23:05 AM QQQ 0.0200
9:24:05 AM QQQ 0.0125
9:25:03 AM QQQ 0.0076
9:26:05 AM QQQ 0.0100
9:27:03 AM QQQ 0.0100
9:28:00 AM QQQ 0.0090
9:29:05 AM QQQ 0.0066
9:30:00 AM QQQ 0.0120
9:31:01 AM QQQ 0.0106
9:32:00 AM QQQ 0.0100
9:33:00 AM QQQ 0.0108
9:34:00 AM QQQ 0.0105
9:35:00 AM QQQ 0.0088
9:36:00 AM QQQ 0.0105
9:37:00 AM QQQ 0.0106
9:38:00 AM QQQ 0.0101
9:39:00 AM QQQ 0.0104
9:40:00 AM QQQ 0.0093
9:41:00 AM QQQ 0.0103
9:42:00 AM QQQ 0.0099
9:43:00 AM QQQ 0.0095
9:44:01 AM QQQ 0.0110
9:45:00 AM QQQ 0.0101
9:46:00 AM QQQ 0.0098
9:47:01 AM QQQ 0.0096
9:48:01 AM QQQ 0.0098
9:49:00 AM QQQ 0.0104
9:50:01 AM QQQ 0.0102
9:51:01 AM QQQ 0.0102
9:52:00 AM QQQ 0.0087
9:53:02 AM QQQ 0.0093
9:54:01 AM QQQ 0.0100
9:55:00 AM QQQ 0.0099
9:56:00 AM QQQ 0.0096
9:57:01 AM QQQ 0.0096
9:58:01 AM QQQ 0.0096
9:59:00 AM QQQ 0.0100
10:00:00 AM QQQ 0.0110
10:01:00 AM QQQ 0.0099
10:02:00 AM QQQ 0.0102
10:03:00 AM QQQ 0.0096
10:04:00 AM QQQ 0.0099
10:05:00 AM QQQ 0.0101
10:06:00 AM QQQ 0.0099
10:07:01 AM QQQ 0.0103
10:08:02 AM QQQ 0.0099
10:09:02 AM QQQ 0.0110
10:10:02 AM QQQ 0.0100
10:11:00 AM QQQ 0.0102
10:12:01 AM QQQ 0.0090
10:13:00 AM QQQ 0.0099
10:14:01 AM QQQ 0.0100
10:15:00 AM QQQ 0.0099
10:16:00 AM QQQ 0.0099
10:17:00 AM QQQ 0.0099
10:18:01 AM QQQ 0.0100
10:19:01 AM QQQ 0.0101
10:20:00 AM QQQ 0.0094
10:21:01 AM QQQ 0.0097
10:22:00 AM QQQ 0.0100
10:23:01 AM QQQ 0.0098
10:24:00 AM QQQ 0.0100
10:25:01 AM QQQ 0.0099
10:26:01 AM QQQ 0.0100
10:27:00 AM QQQ 0.0098
10:28:01 AM QQQ 0.0099
10:29:00 AM QQQ 0.0099
10:30:01 AM QQQ 0.0100
10:31:01 AM QQQ 0.0101
10:32:00 AM QQQ 0.0085
10:33:00 AM QQQ 0.0101
10:34:01 AM QQQ 0.0100
10:35:02 AM QQQ 0.0097
10:36:01 AM QQQ 0.0100
10:37:01 AM QQQ 0.0098
10:38:01 AM QQQ 0.0091
10:39:00 AM QQQ 0.0099
10:40:01 AM QQQ 0.0099
10:41:01 AM QQQ 0.0095
10:42:00 AM QQQ 0.0099
10:43:00 AM QQQ 0.0100
10:44:00 AM QQQ 0.0099
10:45:00 AM QQQ 0.0097
10:46:00 AM QQQ 0.0099
10:47:01 AM QQQ 0.0099
10:48:00 AM QQQ 0.0093
10:49:01 AM QQQ 0.0101
10:50:00 AM QQQ 0.0087
10:51:00 AM QQQ 0.0101
10:52:01 AM QQQ 0.0093
10:53:00 AM QQQ 0.0110
10:54:00 AM QQQ 0.0093
10:55:01 AM QQQ 0.0102
10:56:03 AM QQQ 0.0095
10:57:01 AM QQQ 0.0088
10:58:00 AM QQQ 0.0098
10:59:00 AM QQQ 0.0098
11:00:00 AM QQQ 0.0102
11:01:01 AM QQQ 0.0107
11:02:01 AM QQQ 0.0102
11:03:01 AM QQQ 0.0100
11:04:01 AM QQQ 0.0099
11:05:02 AM QQQ 0.0092
11:06:01 AM QQQ 0.0101
11:07:01 AM QQQ 0.0104
11:08:05 AM QQQ 0.0100
11:09:01 AM QQQ 0.0095
11:10:00 AM QQQ 0.0108
11:11:00 AM QQQ 0.0103
11:12:01 AM QQQ 0.0100
11:13:01 AM QQQ 0.0100
11:14:02 AM QQQ 0.0098
11:15:01 AM QQQ 0.0100
11:16:00 AM QQQ 0.0096
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Trevor Trinkino Quantitative Trader

Machine Learning FXCM Webinar with Trevor Trinkino of CloudQuant – Part 2/3

On May 15th Trevor Trinkino presented part two of a three-part Machine Learning webinar with FXCM. Part one is here.

Part 2  – Preprocess data for Random Forest. PnL and prediciton improvements…

In part two Trevor goes over how to clean and pre-process data from CloudQuant to use in a Random Forest Classifier. He then looks at the prediction and PnL improvements seen from utilizing this classifier as well as adjusting the hyper-parameters of the model. Files mentioned in the video are available from our Google Drive below.. Ipython Notebook Trades.csv FilteredClfFeatsBF100_50.csv MLClassData.csv    
Trading Algorithms on CloudQuant

CloudQuant releases new version of free Algorithmic Strategy Backtesting Tools

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Graphical Analysis of Algorithmic Trading Strategies

The latest version of our Trading Strategy Incubator’s free algorithmic development and backtesting tools adds improved Graphical Analysis of Algorithmic Trading Strategies. This interactive upgrade will allow the algo developer to quickly:
  • Daily Profit and Loss of the simulated algorithmic trading
  • Cumulative Profit and Loss
  • Trading comparisons to major indexes
Additional new functionality includes:
  • Hover Over features to see daily statistics
  • Zoom (in & out) features
  • faster loading of the backtest page
Trading Algorithms on CloudQuant  

Graphical Feedback on Daily Statistics Tabs

Simulated Trades on Daily Candlestick Chart

More detail graphical feedback on the Daily Statistics Tabs allow the user to see Entry & Exit for a stock on a daily minute bar chart. This will allow the quantitative analyst to see missed opportunities. This graph has the ability to be expanded out to a new browser tab. This feature is available on the Closed Trades, Orders, and Executions daily tabs. Technical traders and those using Technical Analysis in their algorithmic trading will be able to see how their signals resulted in simulated trades. Algo Trading daily bars with entry and exit points  

Simulated Portfolio P&L Heatmap

The Portfolio Tab also has an upgrade. The portfolio view will give you a heat map of the profit and loss on each symbol for the simulation day.
Quantamental Portfolio Profit & Loss Chart

Quantitative Trading Strategy Portfolio Heatmap

       
Backtest Homepage showing P&L and Sharpe

CloudQuant rolls out Upgrades to Free Stock Market Backtesting System

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CloudQuant, the trading strategy incubator, announces upgrades to our free stock market backtesting system. The web application allows any market enthusiasts to develop a trading strategy using easy to learn Python programming. Anyone who has ever written a spreadsheet macro or a simple program can easily use the system.
Backtest Research Life Cycle for Trading Strategies

Backtesting Trading Strategies

If you knew your trading strategy would work 50% of the time, would you commit your scarce savings to trade it? What if it worked 75% of the time? Backtesting gives one the confidence to know that your trading strategy will work.