1. The Opening Bell Deviation Theory
Overview
The Trade Possibility
Analysis
Results & Conclusions
Trade Possibility:
Percent Change Heat Map
This experiment was to a sensitivity analysis to examine price deviations of 20% or more shortly after the opening bell to determine if significant same day profits can be made consistently. Though observation, stocks that rose more than 20% shortly after the opening bell seemed to rise substantially within the next few hours leading to tremendous profits. The experiment’s hypothesis is that: “stocks that rise more than 20% shortly after the opening bell will lead to tremendous profits within the hour” – this is known as the Opening Bell Deviation (OBD) pattern.
Project Vision:
To prove statistically if this pattern would produce consistent returns by capturing Alpha and modeling the pattern with intraday data
Value Proposition:
Determine a pattern that would provide consistently high returns to investors who are willing to take on higher than normal risk for higher returns
The procedure had four phases. First, every trade possibility was located in TC2000 and recorded in Excel from 1/4/2017 to 4/17/2017. Following this, 1.2 million rows of historical minute stock data was downloaded from Thomson Reuters into Excel. Second, code was written to simulate/backtest the pattern in which stocks were bought and sold depending upon 22,500 combinations of independent variables. Third, data from each scenario was analyzed to determine which scenario produced the most consistent and profitable results. Fourth, a conclusion was drawn based on the analysis of the results of the experiment.
The hypothesis was proven as multiple profitable and consistent trading patterns were discovered from the 22,500 combinations of independent variables that were run in the stock market simulation.
The hypothesis was proven, multiple profitable and consistent patterns were found.
The Good Scenario produced amazing results.
This Scenario turned $30,000 into $45,000 in 4 months
The best weekly yield was 70% on $10,000
High Stop Percent greatly impacts profits
Most of the stock that were gathered were penny stocks which are very volatile. A Stop Percent of -5% was to high and would result in almost every trade stopping out.
Higher stops are needed in trading penny stocks
A trade possibility is a U.S equity that had a high of greater than or equal to 20% of the previous close on a day and had volume greater than or equal to 100,000 on a day
The Heat Map
Each cell in the heat map represents the percent change for the combination of independent variables (for ranges see legend above).
Results are grouped into four categories: Best, Moderate, Loser, Big Loser
Two Scenario examples are shown below, one good one bad
This is not the full Heat map as it would not fit on the board
Example:
Good Scenario
The Good Scenario had a PctOBD of 30%, Tick Range from 10 to 60, Price Range from 4 to 8, Target Percent of 40%, and Stop Percent of -10%.
This Scenario turned $30,000 into $45,000 in 4 months.
Bad Scenario
The Bad Scenario had a PctOBD of 30%, Tick Range from 10 to 120, Price Range from 2 to 4, Target Percent of 40%, and Stop Percent of -5%.
This Scenario turned $30,000 into $12,000 in 4 months.
Obstacles/Weakness
Data
Minute data for specific stocks on specific dates was hard to gather
Data bases with minute tick data can be hard to gain access to
A unique program had to be made just to gather the specific data
Programs
Five different programs were used to gather and analyze the data. This includes TC2000, RStudio, Thomson Reuters, Microsoft Access and Microsoft Excel
This can make it difficult to move and analyze data
Trade Volume
Volume was not accounted at time of buying stocks to see if their were enough share trading at the minute you would buy
Control Variables
Date
Minute Open
Minute High
Minute Low
Minute Close
Volume
Exchange
Independent Variables
Starting Cash
BlockSize
Transaction Cost
PctOBD
Tick Range
Price Range
Target Percent
Stop Percent
Dependent Variables
Net Profit
Dollars Per Trade
Number of Trades
Apricus Biosciences, Inc. is a San Diego based biopharmaceutical company
APRI had a drug that was approved by the government
APRI opened at 20% and reached a high of 178%
Great profit could have been made though trading this stock
Question
Additional Study Areas
Can significant and consistent returns be made shortly after the opening bell though trading penny stocks?
Areas for Additional Study
Expand the number technical indicators used (like Bollinger Bands)
Expand the number of independent variables
Compare the best pattern to a benchmark
Verify statistically the extent impact of various variables
Areas for Improvement
Include fundamental analysis as part of the approach
Find tools that are better than Excel for analysis
Gather more data to be certain of results
Good Scenario
Program
Collect
Independent Variables:
By testing every combination of independent variables, 22,500 scenarios were run to find the best results.
Each cell on the heat map on the right represents a combination of the seven independent variables listed below.
Good Scenario from 1/4/2017 to 4/17/2017.
Orange Line: Cash steadily increased because of regular profits.
Blue Line: This scenario was fairly profitable resulting in spikes on the blue line
TC2000 was used to locate stocks that had a high of greater than or equal to 20% of the previous close and had volume greater than or equal to based upon the hypothesis
TC2000 was also used to capture the previous day close for every trade possibility
RStudio was used to connect to the Tomson Reuters and gather a full day worth of minute tick data (High, Low, Open, Close, Volume) for all 993 different trade possibilities from 8 exchanges
Data from 1/4/2017 to 4/13/2017
100 Calendar Days
68 Trading Days
Microsoft Access was used to store the 7,944 Excel files
Access was also used to create the weighted averages for the minute High, Low, Open, and Close
Data was then brought from Access into Excel for analysis
Stocks were bought when the percent change crossed though the PctOBD and when the other independent variables were within range
Stocks were sold once the target percent or stop was hit
If the target and stop were not hit the stock would sell at the end of the day
It was assumed that the Transaction Cost was 16 dollars per trade
It was assumed that the Starting Cash was $30,000
It was assumed that the BlockSize, cash used per trade, was $10,000
Hypothesis
Bad Scenario
Acknowledgments
PctOBD
Tick Low
Price Low
Target Percent
Stop Percent
Stocks that rise more than 20% shortly after the opening bell will lead to tremendous profits within the hour
This project took began as just a observation and became so much more. I would like to thank Professor Calhoun, Professor Kaufman, Professor Anderson, and Professor Bozdog on their help and recommendations through out this experiment.
Tick High
Price High
Buy
“This experiment
was run
22,500 times”
PctOBD
Tick Low
Tick High
Price Low
Price High
Bad Scenario from 1/4/2017 to 4/17/2017.
Orange Line: Cash steadily decreased because of constant losses.
Blue Line: The Stop Percent of -5% was to high so 88% of the trades stopped out resulting in a flat blue line
20% 60
.01 1
30% 10
120
.50 2
40% 30
390 1 4
50% 2 8 4 ∞
Procedure
Combinations of independent variables resulted in profits and losses as shown in the heat map at right
Collect
Downloaded a full day of minute tick data from 8 exchanges for 4 months for every trade possibility
Trade possibility: high of the stock for the day is greater than or equal to 20%
Outcome
Only 34% of the trades were profitable in this Scenario. However the Target Percent was 4 times greater than the Stop Percent so profits were bigger than losses resulting in a Good Scenario
Develop & Execute
Simulated/backtested the pattern in Excel using the historical data in which stocks were bought and sold depending upon the variables
Sell
Develop & Execute
Target Percent
Stop Percent
Analyze
Analyzed data from experiment in Microsoft Excel
Reviewed profits based upon combinations of independent variables 5%
-5%
-10%
-20%
-30%
-40%
10%
20%
Outcome
88% of trades were losses because the Stop Percent was too high at -5%. The stocks traded were to volatile and often hit the stop before it could take a profit
30%
40%
Conclude
Organized scenarios from best to worst
Determined the variables that impacted how well a scenario performed