Using Veritas to Construct the "Per…

29-04-2017 Hits:88378 BoomBustBlog Reggie Middleton

Using Veritas to Construct the "Perfect" Digital Investment Portfolio" & How to Value "Hard to Value" tokens, Pt 1

The golden grail of investing is to find that investable asset that provides the greatest reward with the least risk. Alas, despite how commonsensical that precept seems to be, many...

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The Veritas 2017 Token Offering Summary …

15-04-2017 Hits:82105 BoomBustBlog Reggie Middleton

The Veritas 2017 Token Offering Summary Available For Download and Sharing

The Veritas Offering Summary is now available for download, which packs all the information about Veritas in a single page. A step by step guide to purchasing Veritas can be downloaded here.

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What Happens When the Fund Fee Fight Hit…

10-04-2017 Hits:81985 BoomBustBlog Reggie Middleton

What Happens When the Fund Fee Fight Hits the Blockchain

A hedge fund recently made news by securitizing its LP units as Ethereum-based tokens and selling them as tradeable (thereby liquid) assets. This brings technology to the VC industry that...

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Veritaseum: The ICO That's Ushering in t…

07-04-2017 Hits:86484 BoomBustBlog Reggie Middleton

Veritaseum: The ICO That's Ushering in the Era of P2P Capital Markets

Veritaseum is in the process of building peer-to-peer capital markets that enable financial and value market participants to deal directly with each other on a counterparty risk-free basis in lieu...

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This Is Ground Zero for the 2017 Veritas…

03-04-2017 Hits:82928 BoomBustBlog Reggie Middleton

This Is Ground Zero for the 2017 Veritas Offering. Are You Ready to Get Your Key to the P2P Capital Markets?

This is the link to the Veritas Crowdsale landing page. Here is where you will be able to buy the Veritas ICO when it is launched in mid-April. Below, please...

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What is the Value Proposition For Verita…

01-04-2017 Hits:85059 BoomBustBlog Reggie Middleton

What is the Value Proposition For Veritas, Veritaseum's Software Token?

 A YouTube commenter asked a very good question that we will like to take some time to answer. The question was, verbatim: I've watched your video and gone through the slides. The exchange...

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This Real Estate Bubble, Like Some Relat…

28-03-2017 Hits:56155 BoomBustBlog Reggie Middleton

This Real Estate Bubble, Like Some Relationships, Is Complicated...

CNBC reports US home prices rise 5.9 percent to 31-month high in January according to S&P CoreLogic Case-Shiller. This puts the 20 city index close to an all time high, including...

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Bloomberg Chimes In With My Warnings As …

28-03-2017 Hits:84381 BoomBustBlog Reggie Middleton

Bloomberg Chimes In With My Warnings As Landlords Offer First Time Ever Concessions to Retail Renters

Over the last quarter I've been warning about the significant weakness in retailers and the retail real estate that most occupy (links supplied below). Now, Bloomberg reports: Manhattan Landlords Are Offering...

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Our Apple Analysis This Week - This Comp…

27-03-2017 Hits:84096 BoomBustBlog Reggie Middleton

Our Apple Analysis This Week - This Company Is Not What Most Think It IS

We will releasing our Apple forensic analysis and valuation this week for subscribers (click here to subscribe - lowest tier is the same as a Netflix subscription). As can be...

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The Country's First Newly Elected Lame D…

27-03-2017 Hits:83965 BoomBustBlog Reggie Middleton

The Country's First Newly Elected Lame Duck President Will Cause Massive Reversal Of Speculative Gains

Note: Subscribers should reference  the paywall material here for stocks that should give a good risk/reward scenario for bearish trades. The Trump administration's legislative outlook is effectively a political desert, with...

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Sears Finally Throws In The Towel Exactl…

22-03-2017 Hits:90427 BoomBustBlog Reggie Middleton

Sears Finally Throws In The Towel Exactly When I Predicted "has ‘substantial doubt’ about its future"

My prediction of Sears collapsing once interest rates started ticking upwards was absolutely on point.

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The Transformation of Television in Amer…

21-03-2017 Hits:87999 BoomBustBlog Reggie Middleton

The Transformation of Television in America and Worldwide

TV has changed more in the past 10 years than it has since it's inception nearly 100 years ago This change is profound, and the primary benefactors look and act...

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To further the search for the PPT, I though it would be of use to bring the lay readers of the site up to speed on Algo (algorithm) trading. See "Market Manipulation from the Big Boys: Is there really a PPT (Plunge Protection Team)?", "More on market manipulation, guess who..." and "Money flows of market manipulators" for background posts.

 

Algorithmic trading can be defined as placing a buy or sell order of a defined quantity into a quantitative model that automatically generates the timing of orders and the size of orders based on goals specified by the parameters and constraints of the algorithm.

Process

  • Pre-trade analytics - thorough analysis of historical data and current price and volume data to help clients determine where to send orders and when; whether to use algorithms or trade an order manually. Traders can select varying levels of aggressiveness and visualize them against the time horizon for completing the trade. Most compare the spread between bid and ask prices, reference that against the volatility of a given stock, and attempt to create a range of potential outcomes. A lot of the broker-sponsored algorithmic trading systems attempt to measure or project the trade costs.
  • Execution stage - traders create the lists of stocks, choose a particular strategy and enter the start time and the end time. Traders can also monitor the performance and progress of the algorithms in real time and change the parameters if the stock is moving away. Additionally, users can filter portfolios by sector, market cap, exchange, basket, and percent of volume, profit and loss per share.
  • Post-trade analytics - track commissions and assist in uncovering the costs involved from the time a trade is initiated all the way through to execution. Post-trade analytics are meant to improve execution quality and facilitate the making of investment decisions.

The basic and the most commonly used algorithms are: arrival price, time weighted average price (TWAP), volume weighted average price (VWAP), market-on-close (MOC), and implementation shortfall (the difference between the share-weighted average execution price and the mid-quote at the point of first entry for market or discretionary orders). Arrival price is the midpoint of the bid-offer spread at order-receipt time, and it also notes the speed of the execution. VWAP is calculated by adding the dollars traded for every transaction in terms of price and multiplying that by shares traded, and then dividing that by the total shares traded for the day. MOC measures the last price obtained by a trader at the end of the day against the last price reported by the exchange. Implementation shortfall is a model that weighs the urgency of executing a trade against the risk of moving the stock.

Popular algorithmic trading strategies -

  • Iceberging - the common strategy to slice orders into smaller sizes with the intention of hiding, a large order. The maximum amount of shares to be bought at any one time and during a certain sub-period will be specified by the fund manager. For fund managers to build a stake in a particular company and hide the extent of his accumulation, such a technique is useful.
  • Volatility Limit algorithm will take the user's assumptions on volatility, interest rates and dividends to monitor the market and sweep all liquidity when marketable.
  • Pegging - An order is sent out at the best bid (ask) if buying (selling) and if the price moves the order is modified accordingly.
  • Simple time slicing - The order is split up and market orders are sent at regular time intervals.
  • Guerilla - Slicing orders into smaller sizes can also be done with the intention of minimizing market impact. "Guerrilla", an algorithm, developed by Credit Suisse, for example, attempts to determine in real time which publicly displayed (that is those on an exchange or trading platform) bids or offers can be hit or taken without a high likelihood of causing jumps or a displacement in the stock's trading patterns. The technique is useful for fund managers wanting to avoid moving prices against themselves.
  • Participating strategies can be used to ensure that a certain proportion of the trading volume in a particular stock is captured. The algorithm then assures that the required proportion of trading volume is achieved. Such strategies may appeal to momentum-based investors and fund managers who placing an emphasis on trends in volume as an indicator that often corroborate price trends.
  • Benchmark algorithms can be used to achieve a specific benchmark, such as the volume weighted average price over a certain time period. For such investors, the shorter latency (that is, the lag between placing an order and it being implemented) of algorithmic trades compared with those using more traditional methods will help avoid any slippage between the price movements of an index and the constituent components.
  • Market Making - Market making involves placing a limit order to sell (or offer) above the current market price or a buy limit order (or bid) below the current price in order to benefit from the bid-ask spread.
  • Smart order routing - With such algorithms, liquidity from many different sources (conventional trading platforms and dark pools) is aggregated and orders are sent out to the destination offering the best price or liquidity. "Sniper" and "Sharks" are algorithms, developed to detect such hidden sources of liquidity. They detect large orders by putting small market orders to buy and sell.

 

Many of the algorithms used in the market have been developed by investment banks and are supplied to their fund manager clients. This raises the risk of users of algorithms "gaming" the system. For example, an algorithm may trigger a buy order on a certain percentage upward movement in a share price. But if such systems become widely used, then triggering such an algorithm can be a useful way of generating a better market price into which to sell.

 

There are option algorithms as well along with the ability to auto- hedge that is automatically execute equity hedges in real time as the option order is filled.

 

Leading algorithms

 

Dagger from Citi, Guerilla and Sniper algorithms from Credit Suisse, Sonar from Goldman Sachs, the Raider algorithm from ITG and the Tap algorithm from UBS. Most of these algorithms focus on liquidity opportunities in both displayed venues and dark pools.

 

Implications of Algorithmic trading

  • Volatility in the markets has increased since algorithmic trading allows even the smallest of trades to influence stock prices. This has been quite evident lately as volume and diversity of buyers has materially decreased even though stock prices are significantly increasing.
  • One of the key implications of algorithmic trading is the proliferation of dark pools, a type of alternative trading systems or electronic trading venues where money managers trade large blocks of shares anonymously. These dark pools had grown because they're faster, cheaper and open to algorithms especially during volatile periods.

Dark pools have less-stringent requirements and don't have to report monthly volumes or print bids and offers. In response to potential investor protection and market integrity concerns raised by exchanges, the SEC Chairman recently announced better oversight of these dark pools which might include reporting of monthly volumes. According to Goldman Sachs, these dark pools represent 10% of the total stock volume. Some of the largest dark pools include Goldman's Sigma X, GETCO's Execution Services, and Credit Suisse's CrossFinder.

In the news of algo trading today: A Goldman Trading Scandal?

Did someone try to steal Goldman Sachs' secret sauce?

While most in the United States were celebrating the Fourth of July holiday, a Russian immigrant living in New Jersey was being held on federal charges of stealing secret computer trading codes from a major New York-based financial institution. Authorities did not identify the firm, but sources say that institution is none other than Goldman Sachs cnbc_comboQuoteMove('popup_gs_ID0ESF15839609') cnbc_quoteComponent_init_getData("gs","WSODQ_COMPONENT_GS_ID0ESF15839609","WSODQ","true","ID0ESF15839609","off","false","inLineQuote"); .

The charges, if proven, are significant because the codes that the accused, Sergey Aleynikov, tried to steal are the secret sauce to Goldman's automated stock and commodities trading business. Federal authorities contend the computer codes and related-trading files that Aleynikov uploaded to a German-based website help this major financial institution generate millions of dollars in profits each year.

 

 

 

The platform is one of the things that gives Goldman an advantage over the competition when it comes to the rapid-fire trading of stocks and commodities. Federal authorities say the platform quickly processes rapid developments in the markets and using secret mathematical formulas, allows the firm to make highly-profitable automated trades.

The criminal case has the potential to shed a light on the inner workings of an important profit center for Goldman and other Wall Street firms. The charges also raise serious questions about the safeguards that Wall Street firms deploy to protect these costly-to-build proprietary trading systems.

The criminal case began to unfold on the evening of July 3,