mret case studies 5.16(2)-4
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MICROSTRUCTURE RESEARCH & ENGINEERING TECHNOLOGIES, LLC
CASE STUDIESInnovative Solutions for Complex Quantitative Problems
44 Wall Street, 20th FloorNew York, NY 10005
contact@microstructure-research.comwww.microstructure-research.com
646.389.3856
MRET
CASE STUDY 1: Automated Market Maker Looking for Alpha
CASE STUDY 2: Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
CASE STUDY 3: Back-Office Automation & Infrastructure Products for a Broker-Dealer
CONFIDENTIAL
MRET
CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Problem OverviewA designated market making firm that handles retail order flow aims to increase volume to 100 million shares per day and achieve net profits in excess of .004 cents per share.
What Can We Do for this Market Making Firm?
We provide a comprehensive, transparent quantitative research and statistical computing solution to help solve the automated dealer problem. Below is a flow chart illustrating our methodology in dealing with such a statistical computing problem.
MRET
CONFIDENTIAL
Research Dealer Problem
Code Factors in R/Create SQL Access Functions
Formulate Initial Hypotheses/Define Assumptions
Brainstorm Factors that Affect
Dealer PnL
Pseudo-Code Factors/Find Right Packages & Libs
Run Principal ComponentAnalysis (PCA) on Initial
Factor Dictionary
Fit the Data (Simple,Multiple, Non-Linear
RegressionConstruct Factor Dictionary
Prepare the Data/Acquire, Load/Clean...
Utilize Machine LearningAnd Artif. Intel. Techniques
Implement Regression and Logistic Tree Analysis
Create Model PnL Configure High Perf.Computing Environment
(8 Core, 16GHZ, GB Ram, Linux Servers)
Build Requisite Utilities
Classification and Sensitivity Analysis
Case Study 1 Automated Market Maker Looking for Alpha
MRET
CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Define the Dealer Problem Formally
● Using an options pricing framework (Copeland and Galai, 1983) we can reduce the complex trade-off between
the spread that uninformed traders are willing to pay for liquidity versus the spread that aggressive informed
traders are willing to pay for liquidity to an optimal quoted price that represents the liquidity premium that the
dealer charges to traders for immediacy.
● To Solve the Dealer Problem, the dealer must beat Gambler’s Ruin by earning more from uninformed traders
than is lost to informed traders. This is done by earning a positive bid-ask spread, generating cash while
maintaining inventory.
● We want to first know what combination of factors related to the dealer’s order flow and market behavior
maximize the dealer’s risk adjusted return curve.
● The dealer must achieve a positive expectancy per share net of .004 cps in costs.
● How will the outcome be judged? The model will be judged by how accurately its output fits the market
maker’s ideal return curve.
Research the Dealer Problem
MRET
CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Figure 1
Figure 1 illustrates the price of the demand for immediate liquidity in both efficient and inefficient markets. In efficient markets where information is distributed evenly and instantaneously across all market participants shares transact at the fundamental value given by Vt; however, in inefficient markets where information is asymmetrically distributed the monopolistic dealer has pure pricing power and trade prices given by K*(Q, λ) diverge from Vt and are a function of order quantity Q in relation to arrival rates of traders given by λ (Chacko, C., Jurek, J., Stafford, E., 2008).
Research the Dealer Problem
MRET
CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
● The schedule of limit prices guaranteeing immediacy depends on the factors determining the value of
the dealer’s option such as arrival rate of opposing order flow λi(·) and limit order prices relative to the
fundamental value of the stock Vt.
● Stoikov and Avellaneda (2006) suggest that
● the optimal bid price may be given by:
● the optimal ask price may be given by:
● Liquidity price schedules can then be
estimated as a percentage immediacy
cost for sales and purchases given by:
Research the Dealer Problem
Compile Relevant Theoretical Papers
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
● Look at the data.
● Determine PnL/Share. Assume exit price after a fill is at VWAP.
● Plot each factor individually against PnL/Share.
● Plot all factors against PnL.
● Determine significant effects.
Collect data and examine them. What are the relevant factors? What are the relationships between the
relevant factors and the dependent variables?
Research the Dealer Problem -Compute PnL from Order Flow Data
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
H1: Liquidity is a function of a security’s fundamental value at a particular point in time and characteristics related to order flow. H2: Liquidity demand can be priced by using an options framework that characterizes concessions of traders seeking immediate liquidity for various transaction sizes.
H3: The most relevant factors affecting the price of liquidity are measurements of price, volatility, volume, and trading intensity.
H4: The liquidity premium that the dealer charges represents a large portion of the transaction costs incurred by traders/investors.
H5: Adverse selection cost when dealing to informed traders represents the dealer’s greatest expense.
H6: The dealer’s price setting power is a function of order arrival rates. The greater the demand for liquidity by uninformed traders, the more money the dealer makes.
H7: Widening Bid-Ask spreads are increasing functions of volatility.
H8: Widening Bid-Ask spreads are increasing functions of covariance in order flow.
H9: Subtle biases in price prior to order receipt may gauge adverse selection risk.
H10: Market impact is a non-linear concave function of transaction size.
H11: The price of liquidity is also a non-linear concave function of transaction size.
Formulate Initial Hypotheses and Assumptions
Initial Hypotheses
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
A5: Market is organized around a single market maker that acts as both an intermediary and an agent that trades
shares as a broker and from his own inventory as a dealer. If a traders wants to buy Q shares and there is an order
imbalance of q shares, the bid-ask spread is crossed for that quantity. The residual quantity is given by Q = Q-q.
A6: The probability of observing an order imbalance of Q shares in the next period is given by:
Accordingly, the estimated time for the completion of a Q share limit order to sell (buy) is distributed exponentially with mean:
Formulate Initial Hypotheses and Assumptions
Basic Assumptions
A1: Volatility increases the cost of transactions.
A2: Option maturities of the dealer’s quotes determine arrival rates.
A3: Order flows are stochastic.
A4: Markets are sometimes efficient and sometimes inefficient.
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
● Average Cost of Instantaneous Reversal-Estimates Fundamental Value
● Log Price Distributions and Patterns (Momentum/Reversal/Auto-Correlation)
● Log Volume Distributions
● Illiquidity Ratio and Measures
● Profitability of Limit Orders
● Log Range (Volatility)
● Bid-Ask Spread Estimates (Log)
● Estimated Order Arrival Rates (Buy/Sell)
Brainstorm Factors
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Construct Initial Factor Dictionary
1a. Log Returns
Log Returns are useful to analyze financial time series as they are easy to work with and more accurate in
describing actual distributions of asset prices as compared to simple returns.
Asynchronous data must be equally spaced.
Definition:
Factor 1: Distributions of Returns
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 1: Distributions of Returns
1b. Average Returns Over Various Evaluation Periods
Arithmetic averages can yield lower frequency return estimates. Moving averages can also be used to
estimate order book skewness which in turn could help estimate price sensitivity to market orders and
market depth.
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
1c. Volatility of Returns over Various Evaluation Periods
Variance of Simple and Log Returns:
Factor 1: Distributions of Returns
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 1: Distributions of Returns
1d. Skewness of Price Distribution Over Various Evaluation Periods
Skewness measures whether a distribution skews towards either positive or negative side of them mean of the price distribution. This describes order book shape.
Definition:
1e. Kurtosis (Fat Tails) of Price Distribution Over Various Evaluation Periods
The fatter the tails of a return distribution, the higher the chance of an extreme positive or negative return.
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 2: Auto-Correlation
Auto-correlation using log returns can indicate a persistent behavior in returns
and validate patterns of momentum or reversal over a particular evaluation
frequency. Auto-correlation p(p) of order p of a log return distribution can
range from -1 to 1. High auto-correlation >0.5 implies a positive relationship
between observations and lagged observations.
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 2: Auto-Correlation
Testing the statistical significance of the observed auto-correlation at a given lag:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Limit orders filled at x% above/below the market should have a greater average profitability over a
given evaluation period than market orders over the same period. A buy limit order strategy, for
example, is profitable if the average cost of realized buy limit orders is lower than the average cost of
buy market orders.
Factor 3: Profitability of Limit Orders
Preconditions:
● Segregate data into discretized periods of evaluation for profitability analysis.
● Market orders are executed at the start of the evaluation period.
● Limit orders are placed at X% below/above opening price of evaluation period.
● Buy Limit Order is filled when Low Ask Price < Buy Limit Order Price.
● Sell Limit Order is filled when High Bid Price > Sell Limit Order Price.
● If Limit Order is not filled in the evaluation period, the Limit Order is executed at the opening price of the next evaluation period.
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 3: Profitability of Limit Orders
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Take evaluation period’s average price range divided by an evaluation
period’s standard deviation of average price range and adjust by square root of number of
observations in a year. Good strategies at the highest frequency have the highest risk
adjusted profitability and yield a double digit Sharpe Ratio. This can be used as a
comparative benchmark.
Factor 4: Market Opportunity (Maximum Possible Annualized Sharpe Ratio)
Compare maximum Sharpe Ratio of strategy with a strategy of perfect predictability over various evaluation periods. See next page. . .
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 4: Market Opportunity (Maximum Possible Annualized Sharpe Ratio)
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 5: Market Impact and Illiquidity Ratio
Higher illiquidity correlates to higher expected returns and greater market inefficiency. Below, λt is
proxy for market impact.
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 6: Price Sensitivity to Block Transactions
The smaller the sensitivity to transaction size, represented by λ, the larger the market’s capacity
to absorb market orders at the current market price.
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Cost of instantaneous reversal in price for a given order quantity relates to profitability of market orders
submitted by uninformed traders over a given evaluation period. This factor is estimated using a tightness
measure of the bid-ask spread. Limit orders tend to fare better in lower liquidity environments and this can be
assessed by the average cost of instantaneous reversal.
Factor 7: Average Cost of Instantaneous Reversal Over Various Evaluation Periods
Definition:
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Definition:
Factor 8: Market Inefficiency -Non-Parametric Runs (By Broker ID, Time of Day, Trade Size)
Sequential runs of trades with the same sign represent inefficiency and thus an alpha generation trading
opportunity. Based on factor 2, if the desired trading frequency (that dictates target Sharpe Ratio) is 1 second,
then a run is a consecutive set of price movements with the same sign that occurs at 1 second intervals. P4
would equate to the 4th run of positive sequential price movements, for example. N7 is the 7th run of negative
sequential price movements.
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 8: Market Inefficiency -Non-Parametric Runs (By Broker ID, Time of Day, Trade Size)
Definition Continued
Test for Randomness
Standard Deviation of Runs
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Runs at the specified evaluation interval are predictable with a 95% statistical
confidence if the number of runs is at least 1.654 standard deviations away from the
mean x bar.
The number of runs is not random if the two tailed test based on the Z score is
rejected.
_
Z = (|u – x| - 0.5)/s
Thus, the randomness of runs is rejected whenever the Z score is > 1.654.
Test for Statistical Non-Randomness
Definition Continued
Factor 8: Market Inefficiency -Non-Parametric Runs (By Broker ID, Time of Day, Trade Size)
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 8: Market Inefficiency -Non-Parametric Runs (By Broker ID, Time of Day, Trade Size)
Output Results Table Example
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 9: Bid-Ask Spread Estimates
Bid-Ask spreads vary according to intraday seasonality and volatility patterns and can be a
proxy for liquidity. Spreads widen during periods of market instability and contract when
markets are calm. Wider spreads represent decreased liquidity, increased volatility and
increased costs to traders and wider spreads decrease traders’ overall profitability. The
profitability of market orders should increase from the perspective of liquidity providing market
makers as increased volatility and decreased liquidity present an opportunity to profit from
uninformed participants. Spreads can be estimated by looking at the price of an asset at a given
time represented by pt that equals some fundamental value plus half of the bid ask spread, s.
The fundamental value is increased by s if the subsequent market order is a buy and the
fundamental value is decreased by s if the subsequent market order is a sell.
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 9: Bid-Ask Spread Estimates
Definition (Roll, 1984):
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 10: Effective Bid-Ask Spread
Definition
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 11: Adverse Selection Fraction of the Bid-Ask Spread
The bid-ask spread is comprised of three costs- Order Processing, Inventory and Averse Selection.
Below, order processing and inventory costs are aggregated and we focus on the adverse selection
cost component of the bid-ask spread. We are estimating λi the fraction of the traded spread that
accounts for adverse selection cost.
Order Processing Costs:
Costs specific to the market maker including exchange fees, settlement and trading costs, transfer
taxes, rents etc. To transfer these costs to trading counterparties (namely uninformed traders), the
market maker should increase its spread by a certain fraction.
Inventory Costs:
When accumulating inventory at suboptimal prices, the dealer must increase the bid-ask spread
charged to slow the rate of accumulating unprofitable inventory until the inventory can be
distributed profitably.
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Adverse Selection Cost:
The market maker always loses to better informed traders possessing asymmetric information. The dealer needs
to hedge out the risk associated with trading against informed traders by appropriately increasing the spread it
charges to all counter-parties so as to be compensated for incurring adverse selection risk as the primary cost of
providing liquidity. The primary tradeoff that the dealer faces that determines its profitability (beating
Gambler’s Ruin and achieving a net positive expectancy per share) is that it must earn more from uninformed
traders than it loses to informed traders.
Factor 11: Adverse Selection Fraction of the Bid-Ask Spread
Definition
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 12: Adverse Selection Fraction of the Bid-Ask Spread
The vector auto-regressive model measures presence of asymmetric information and estimates
information – based impact given by λ.
Definition
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Factor 13: Duration
Estimate the factors that affect the duration between sequential ticks as the interval between quote
arrivals contains valuable information. Analyze both quote and trade arrival patterns. Such arrivals
follow a Poisson process. Duration models suggest that the shorter the duration between trades the
higher the likelihood for pending good news. On the contrary, the longer the duration between trades,
the higher the likelihood for bad news. . .
Construct Initial Factor Dictionary
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Pseudo-Coding
Pseudo-Coding is a collaborative Process between MRET and the Client.
MRET
CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Pseudo-Coding
Pseudo-Coding is a collaborative Process between MRET and the Client.
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
To-Dos
<Clean Sample Data> Completed
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Output Sample Factor Values for Evaluation Period 10 Bars
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Run Principal Component Analysis (PCA)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Run Principal Component Analysis (PCA)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Run Principal Component Analysis (PCA)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report PCA Output for Factors (Sample for Evaluation Period 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Report MLR Output for Principal Factors (Sample 10 Bars)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Status Updates Maintain Transparency & Proper Project Direction
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Classification (Sensitivity Analysis)
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CONFIDENTIALCase Study 1 Automated Market Maker Looking for Alpha
Classification (Sensitivity Analysis)
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CASE STUDY 1: Automated Market Maker Looking for Alpha
CASE STUDY 2: Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
CASE STUDY 3: Back-Office Automation & Diagnostic Tools
MRET
CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
What can we do for this Hedge Fund?We will engineer an automated trading system according to the specifications of the client and provide ongoing support for the optimization of both the strategy and the automated trading system performance.
Problem OverviewAutomate an oil futures arbitrage strategy to generate entry and exit points and hedge orders across multiple exchanges.
MRET
CONFIDENTIAL
Research the Dealer Problem
Case Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Research Trading Concept
Code
Explore Client Objective& Gather Requirements
Create a Development Plan
Pseudo-Code
Implementation
Data Pre-Processing
Logging & Order Audit Trail
Provide PreliminarySchedule
Revise Code
Back Test
Probationary Testing
User Acceptance, Documentation
& Ongoing Work
MRET
CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Explore Client Objectives & Requirements
Replace Manual Trading performed on the CME/NYMEX Exchanges.
Initial Requirements
● Exchange & Future Contract Information
● Offsetting Differential
● Margin Constraints
● Volume (Trade, Show)
● Technical (XTAPI, C#, .NET (Specified by Client in this Case))
● Noise Alerts
● User Documentation
● *C# Visual .NET for this initial strategy prototype as specified by Client.
●Organize the Manual Trading System's Rules●Automate Rules into Code●Execute Strategy
*MRET is Platform and Environment Neutral
MRET
CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Create a Development Plan
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Create a Development Plan
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Preliminary Development Schedule
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
● A collaborative process
● Conceptualize and Formulate the Trading Strategy such that everyone is on the same page.
● Specify trading rule.
Develop Pseudo-Code
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Code Snippets
Code
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Code Revision
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Data Pre-Processing & Logging
Data Pre-Processing
● Liquidity Test/Sufficient Market Depth.
● Liquidity Data Set.
● Requisite Order Flags.
Logging & Order Audit Trail
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Back Test Results
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Probationary Testing, Identify Errors, Debug and Re-Test
Overview
● Partial Fill.
Breakdown
● Fix the issue of totalQuantity and now strategy stops as soon as we reach total quantity.
● Unit testing of partialFill.
● Unit testing of totalQuantity.
Next Plan
● Unit Testing of Max Position.
● Questions.
● NaN.
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
Probationary Testing, Identify Errors, Debug and Re-Test
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
User Interface
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
User Documentation
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
User Documentation
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
User Documentation
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CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
User Documentation
MRET
CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
User Documentation
MRET
CONFIDENTIALCase Study 2 Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
User Acceptance Testing & Ongoing Work...
MRET
CASE STUDY 1: Automated Market Maker Looking for Alpha
CASE STUDY 2: Energy Hedge Fund Seeking to Automate Latency Sensitive Arbitrage Strategy
CASE STUDY 3: Back-Office Automation & Infrastructure Products for a Broker-Dealer
CONFIDENTIAL
MRET
CONFIDENTIAL
Problem Overview1) Broker/Dealer needs to Stress Test Automated Trading Infrastructure.
2) A Broker/Dealer needs to build out its back-office compliance and risk management of its proprietary traders due to increased regulation.
What can we do for this Broker/Dealer?
We can build customized tools to stress test its automated trading system and develop products for its back-office compliance and risk management needs.
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
MRET
CONFIDENTIALCase Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
Product Research
Understanding Existing Back-Office Processes
Understanding Key Participants
Understanding How Components Interact
Develop Web Portal and/orDesktop Applications
Understanding SOR, OMS & Compliance Systems
END PRODUCTS
Implementation
Design Relational Schema
Compliance/Risk Tool Reconciliation Engine
Order Spammer
Draft Requirements
Reconciliation Tool
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CONFIDENTIAL
● A Diagnostic Tool for Reconciling a Smart Order Routing System with an Order Management System.
● A Tool that Duplicates Order Flow in Real Time to Two Separate Processing Engines.
● A Component that Generates Log Files for Each Order Flow.
● An Output Compares Results to Identify Mismatches and Possible Erroneous Trades.
Reconciliation Engine
The Reconciliation Engine is part of an Infrastructure Ramp-Up for High Freq. Trading, including:
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Order Spammer
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Order Spammer Documentation
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Order Spammer Documentation
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Reconciliation Tool
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Reconciliation Tool Documentation
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Reconciliation Tool Documentation
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Reconciliation Tool Documentation
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Reconciliation Tool Documentation
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Reconciliation Engine -Reconciliation Tool Documentation
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
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Risk & Compliance UI
● Create Trader Profiles.
● Manage Buying Power.
● Manage Max Position Size.
● Manage Max Order Size.
● Set Thresholds and Alerts for Traders as Well as Firm-Wide.
● Manage Short-Sales and Hard to Borrow List.
● Manage Trader Fee & Rate Schedules.
Manage Trader Risk & Compliance Profile
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
MRET
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Risk & Compliance UI
Case Study 3 Back-Office Automation & Infrastructure Products for a Broker-Dealer
MICROSTRUCTURE RESEARCH & ENGINEERING TECHNOLOGIES, LLC
CASE STUDIESInnovative Solutions for Complex Quantitative Problems
44 Wall Street, 20th FloorNew York, NY 10005
contact@microstructure-research.comwww.microstructure-research.com
646.389.3856