The leverage space trading model pdf

Although numerous Nobel Prizes have been awarded based on some of those widely accepted principles, their popular acceptance does not constitute real-world validation. What has been needed is a viable alternative to directly address these real-world dictates. In The Leverage Space Trading Model, Vince offers a groundbreaking contribution to the literature that builds on a lifetime of expert analysis to deliver not only a superior new portfolio model, but takes the entire discipline of portfolio management to a new level.

In this book, Vince—who has made many important intellectual contributions to the field for over two decades—departs radically from informed orthodoxy to present an entirely new approach to portfolio management. At its core, The Leverage Space Trading Model basically tells how resources should be combined to maximize safety and profitability given the dictates of the real world. But, as the author points out, given the complex and seemingly pathological character of human desires, we are presented with a fascinating puzzle.

Research has found that human beings do not primarily want to maximize gains—our psychological makeup is such that we instead tend to possess seemingly more complex desires. Most simply put, this book will change how you think about money management and portfolio allocations. RALPH VINCE is a computer programmer who got his start in the trading business as a margin clerk and later worked as a consultant programmer to large futures traders and fund managers.

So, is this a good game? From Long-Term Capital Management to the companies behind the mortgage crisis, many financial companies have thought so. The chance of their black swan event may have been less than one in 50, and they certainly bet less than their entire stake with each trade, but the rules of their games were similar. Well, there was one key difference — many of these firms played with the safety net of a government bailout. Real trading is based on a stream of returns, continuous numbers, not coin flips.

We manage these streams by building a probability matrix. The best way to do this is to bin our data. First, we calculate the range of the data and create bins. We then calculate our joint probability tables. We will use the equity curves of three different systems. The first step in building a joint probability table is to process the equity data into differences.

Next, we break the period difference into bins. The max, min and range are needed to do this:. The bins do not have to be equally spaced, but we will do so to simplify our example. Here are the bins:. Each bin is represented by its mid-point. We then record the number of actual occurrences for each of the combinations between the three systems.

The actual number of records and the number of occurrences are used to calculate the probability of each combination. In real data sets, over longer holding periods such as monthly or yearly, we often have many combinations without any occurrences. We can address this by adding additional data or by replacing some of the lower performing cases with worst-case scenario, black swan scenarios. We also must figure how many holding periods these black swans should last so we can create multiple records to simulate a real event.

In our example, we have 13 data records, but combinations across the five bins. This gives most individual records very small probability. Because many of our possible cases did not occur in our data set, we remove these cases from the joint scenarios table. So, our table has been pared down to only 12 rows, not our original possible combinations. At this point, we have all of the information we need to perform the Leverage Space calculations.

Assume we are solving for the f values of 0. We would figure our HPR 0. We can sum these for each row, and obtain a net HPR for that row. This example uses fixed f values for each system. In reality, we would optimize the f value based on given constraints and time horizons. Then, we would select those that meet our constraints. A common set of constraints is to limit drawdowns. The joint probability tables with a given time horizon are used to calculate this. We will perform multiple horizon analysis, as we did for the multiple coin toss game, and calculate returns.

We cap our total wealth relative TWR at 1. This is our final stake after compounding. We then look at HPR for the next time horizon. For example, if TWR is 0. Save to Library Save. Create Alert Alert. Share This Paper. Background Citations. Methods Citations. Citation Type. Has PDF. Publication Type. More Filters. A General Framework for Portfolio Theory.