Nickola,Originally Posted by nickola.pazderic
I use my proprietary software but you can easily use this excellent online spreadsheet often recommended by Maxime. Many forum members are using it with satisfaction. There may be some very minor differences due to database sources. I use Yahoo Finance data.
http://bit.ly/hQdjAs
Billy
Steve,
You may review the 20 DMF methodology. The 20 DMF is not computed with the 500 components of S&P 500, but with over 1,000 stocks from all sectors. The 20 DMF signal is not derived proportionally to the signals from all stocks, but is proportional to the signals from all sectors. Hence, the S&P 500 chart is shown as a reference only for the behavior of large (institutional) cap stocks in relation with the 20 DMF, but the 20 DMF is a much more general market signal. When it is in short mode like now, it tells you to favor short setups over long setups for most instruments and stocks. It tells you a lot about timing, but nothing about the setups and their risks.
What the IWM robot does with a 20 DMF short signal is to automatically look for entering a short position but only with an optimal setup for risk management and with the best statistically expected returns. The optimal entry price is always based on a 3:1 reward-risk ratio from the multi-pivot method. On average, this 3:1 RR method returns 0% risk-adjusted returns in a trendless market. But when a trend bias is identified by the 20 DMF and/or the satistics and we use the same 3:1 reward-risk entry setups, we can achieve the exceptional risk-adjusted returns described in the introductory robot paper.
Paul answered perfectly all your other questions. Thank you Paul!
Billy
Billy, how do you define market makers vs institutions?
Market makers = banks?
institutions = mutual funds / hedge funds?
Also, what do you mean by "vested interest" on the market makers to accumulate GDX positions?
Thanks! Learning in leaps and bounds
Yes, institutional investors are mutual funds, hedge funds, but also pension funds or insurance companies for example. Their common trait is that due to their very large positions, they need to spread their accumulation and distribution over several days. They turn to market makers for executing their orders "at best" within a deadline and optimizing in function of the liquidity available in the market. Market makers fees for such operations are proportional to the actual volume-weighted average price (VWAP) of the position compared to the VWAP of the underlying instrument over the same timeframe. This is the first "vested" interest of market makers : when buying institutional positions, their interest is to push price down enough so as to be able to produce a better VWAP than the recent VWAP by the market. They typically target lower floor levels for that purpose, where they will accumulate in a consolidation, usually panicking the little retail guy along the way and "fishing for stops". Today, all that process is programmed with algorithms in High Frequency Trading (HFT) programs that are giving much weight to the multi-timeframe pivot floor levels as their intermediate price targets.
A market maker is also a broker-dealer firm that accepts the risk of holding a certain number of shares of a particular security in order to facilitate trading in that security. Each market maker competes for customer order flow by displaying buy and sell quotations for a guaranteed number of shares. You are always buying from or selling to a market maker. Once an order is received, the market maker immediately sells from its own inventory or seeks an offsetting order. Actually, market makers are managing for their own accounts inventories with many timeframes pivot and floor levels targets. This is their second vested interest: when, collectively, they see institutional distribution orders rising or large buy orders waiting at much lower prices, they are the only market participants authorized to sell short naked positions for their own accounts. So they sell short while breaking down an important pivot/floor support and they will cover their shorts while approaching the next lower floor support level, rarely before! As long as the lower level is not hit, they will do all they can to facilitate a quick and violent downside move.
Billy
Billy--
Can you recommend books, articles or magazines to teach me about the actions of market makers and institutions?
My interest is as big as my ignorance.
Again, if this has been handled pre-EV Forum, my apologies.
Many thanks,
Nickola,
I am unable to recommend anything, although I am sure some good stuff could be found by digging/googling deep enough.
Most of what is written is of no practical value, especially with market makers true actions. As i've written many times in the past, these are some of the best kept secrets because they are so lucrative.
All i've learned was from actual practice and experience in real life, being a former euro-bond and later NASDAQ market maker myself long before the job became all computer-driven. For example, I NEVER looked at a chart on my market maker screen, I was only interested in the spreadsheets of my clients' and my own inventories' average weighted prices evolution for all timeframes. Like for all market makers, it was all about scaling in and out at each pivot & floor level for each timeframe. The big money was made with the longest timeframes inventories and the ability to practice naked short-selling; pocket money came from the spreads between bid & ask prices.
That's how I came to the confluence/cluster concepts, trying to monitor the collective action of market makers. A lot is written about institutional investors accumulation/distribution, but it is actually executed by market makers with their own techniques and conflicts of interest.
Billy
Billy,
On the Robot page it states:
These are the probabilities for the combined Long term / Short term settings:
Today's SHORT settings are NEUTRAL.
The LT algo found a short edge of -1.25%. This is lower than the -0.75% limit
The ST algo found a relatively weak negative edge of -1.59%
The LT edge is more positive than -2%. In the past this combination led to a 3D short LOSS of -0.11% from the previous day's close. The trade became positive after three days in 53.1% of the cases.
How do these probabilities coincide with the 3:1 risk reward probability you mention above?
The Robots uses the 3:1 risk rewrads and the potential earnings to decide on a short/long position (it compares first the LT edges and then the ST edges).
I have incldued additional statistics so that investors can see what the combination of the LT/ST combination did in the past. This allows to size your position. The robot does not take these into consideration.
Pascal
I think I may be mixing up timeframes.
So far as I understand, the Robot page statistics indicate, based on historical data, what the likelihood is for gain/loss over the next 3-day period. The greater the probability of gain, the larger the position size, all other things being equal.
In conjunction with this shorter timeframe probability, I am to assume so long as the Robot is providing an entry point for that day the trade holds (at least) a 3:1 RR probability with the proviso one enters the trade at the price point given.
Billy,
I wonder why simple averaging of signals from all sectors makes sense for the formation of the 20DMF signal. As sectors' aggregate market caps (and consequently, trading volumes) can be very different, wouldn't simple averaging introduce a skew into the 20DMF signal by over-representing the smaller sectors (and vice versa wrt the large sectors)? I'd be naturally inclined to weigh the sectors' signals in proportion to their market caps, but then, how's that different from just combining 1000 stocks' signals with their respective market caps weights?
Trader D
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