A cut-off will be defined and, according to the performance of the securities over a selected period, a long-short portfolio will be created. In fundamental analysis, factor-based investing is an example of the trend trading approach.

Factor-based investing selects the stocks to invest in by considering the factors that explain the stock returns. These factors include the value, size, volatility, momentum, growth.

In addition to these, macroeconomic factors like inflation, interest rates, Gross Domestic Product GDP are also considered. Stocks selected based on these factors are expected to outperform the markets in the long term. The trends of these factors decide whether to buy or sell a stock.

Mean reverting strategies are designed under the assumption that over time, the prices and the economic indicators move back to their mean. The components of a stock such as prices, volatility, etc. are expected to exhibit mean reversion properties. Buying a stock after a sudden sharp fall below the mean is a basic example of a mean-reverting trading strategy.

Several mean reverting trading strategies are designed using technical analysis. Some major examples are:. When the Bollinger bands are non-trending, the upper and lower bands represent the overbought and oversold levels of the price respectively.

Consider the following example of TSLA. As discussed earlier, the mean reversion strategy works best when the market is non-trending i. range bound. Flat Bollinger bands represent range bound markets and generate buy, sell signals at oversold and overbought levels.

In quantitative analysis, pairs trading and statistical arbitrage strategies are categorised as mean-reverting trading strategies. In fundamental analysis, the value investing strategy is a prime example of mean-reverting strategies. Using fundamental analysis, the intrinsic value of a security is calculated. The security is considered undervalued if it is trading below its intrinsic value. Value investing involves buying securities that are undervalued.

Breakout trading strategies involve buying or selling an asset after breaking important price levels such as long-term support and resistance levels of the stock. In technical analysis, buying after a breach of a resistance level or selling after a breach of a support level comes under a breakout trading strategy.

The technical analysis strategies such as opening range strategy, dual thrust strategy and strategies based on breakout of patterns like wedges, flags, head-and-shoulders and triangles come under breakout strategy.

The below is an example of a falling wedge breakout strategy. Falling wedge is a pattern where the price moves between two downward sloping lines that are converging towards each other. A buy position is taken when price moves above the upper line of the wedge pattern. In the chart below, the security GOOGL has a falling wedge pattern on a daily timeframe in the months of February and March. On April 1, , the breakout of the upper line of the wedge was observed which is a trigger for the buy signal.

In quantitative analysis, advanced quantitative models such as time-series regime switching and hidden Markov models are used to design the breakout strategies. In fundamental analysis, relative value strategies are considered as breakout strategies. Carry trade is designed to make profit from the difference between the interest paid and interest earned. This is widely used in the currency market. In currency markets, the carry trading strategy is executed by selling low-yielding currency and buying high-yielding currency.

The carry strategy is executed by buying the currency pair where the base currency has a higher interest rate than the quote currency. This is called a positive carry trade.

Once the trade is executed, you will receive the difference in the interest rate from the broker as long as the difference is positive. However, you would incur losses If the interest difference turns negative, that is, if interest rate of currency B increased and crossed the interest rate of currency A.

In quantitative analysis, market making and cash-future arbitrage are examples of carry trade strategy. In technical analysis, volatility selling strategies i. short gamma is an example of carry trading. Event based trading strategies are used to take advantage of price inefficiencies that are formed following the release of economic and corporate events.

In quantitative analysis, news-based stock trading is considered as an event based trading strategy. Read the article Quantified News Analytics: Profitability vs Pitfalls to gain deeper understanding on news-based stock trading.

Basically, ML-based trading strategies are the contemporary practice. They are a mix of quantitative, technical and fundamental methods for each of the trading strategies mentioned above namely trending, mean reverting, break out, carry and event based. The supervised learning method for machine learning models consists of two main techniques, that is:. Regression is a statistical process of determining relationships between variables.

It helps one to understand how the value of the dependent variable changes when any one of the independent variables is varied. It also allows comparing the effects of variables measured on different scales, such as the effect of price changes. In trading, regression is used extensively, especially in pairs trading strategy, and when it is required to evaluate the performance of a stock in comparison to market returns.

Linear Regression is one of the most widely known modeling techniques. Linear regression establishes a relationship between a dependent variable Y and one or more independent variables X using a best fit straight line. If there is only one independent variable, then it is called a simple linear regression but if there are more than one independent variables, then it is called multiple linear regression.

For calculating the regression line slope, you can use the Scikit-learn library in Python. Scikit-learn library features algorithms for supervised and unsupervised models that includes regression techniques, classification, clustering, random forest etc. For example, if you calculated a slope of 1. Logistic regression is similar to linear regression, but with only one difference. Logistic regression measures the relationship between the dependent variable and one or more independent variables by estimating probabilities using a logistic function.

Classification is one of the methods which is applied using a Support Vector Classifier SVC technique and is part of an unsupervised learning method in machine learning. The classification technique maps the input into a discrete class or a category as shown in the image below:. For instance, the categories in the trading domain can be classified as entry position and exit position in any of the following markets - a stock, commodity, bond, derivative.

Recommended read: Machine Learning Classification Strategy in Python. Random Forest , also called Random Decision Forests, is a method in machine learning capable of performing both regression and classification tasks.

It is a type of ensemble learning that uses multiple learning algorithms for prediction. Random Forest comprises decision trees , which are graphs of decisions representing their course of action or statistical probability.

These multiple trees are plotted to a single tree called the Classification and Regression CART Model. To classify an object based on its attributes, each tree gives a classification that is said to vote for that class. The forest then chooses the classification with the maximum number of votes.

For regression, it considers the average of the outputs for different trees. Unsupervised learning is a type of machine learning in which only the input data is provided and the output data labelling is absent. Algorithms in unsupervised learning are left without any assistance to find results and in this method of learning, there are no correct or wrong answers. K-Means Clustering is a type of unsupervised machine learning that groups data on the basis of similarities.

K-Means is one technique for finding subgroups within datasets. One difference in K-Means versus that of other clustering methods is that in K-Means, we have a predetermined amount of clusters while other techniques do not require that we predefine the number of clusters. The algorithm begins by randomly assigning each data point to a specific cluster with no one data point being in any two clusters.

It then calculates the centroid, or mean of these points. The object of the algorithm is to reduce the total within-cluster variation. In other words, we want to place each point into a specific cluster, measure the distances from the centroid of that cluster and then take the squared sum of these to get the total within-cluster variation.

Our goal is to reduce this value. The process of assigning data points and calculating the squared distances is continued until there are no more changes in the components of the clusters, or in other words, we have optimally reduced the in-cluster variation. Recommended read: K-Means Clustering Algorithm For Pair Selection In Python. Reinforcement learning is a way to encourage or change a particular unwanted behaviour by the system. Whenever the system gets a reward for giving the desired result as fed to the system , it is positively reinforced and when the system does the opposite of the desired result, it is negatively reinforced.

This way the machine learning system learns. It is very much similar to how human beings learn. When they get the desired result in a field, say, a good score in an examination, they are rewarded with a good job.

Here is a diagrammatic representation of how a machine learning model works with reinforcement learning:. Recommended course: Introduction to machine learning for Trading. Machine learning trading strategies are best applied with the help of popular computer language Python. There is a Python package known as Scikit-learn , which is developed specifically for machine learning and features various classification, regression and clustering algorithms.

The strategies explained in this article can be combined to create a portfolio. For example, you can combine the strategies under fundamental analysis and technical analysis.

Fundamental analysis will be used to select the asset to trade and technical analysis will be used to time the entry. Now, the market moves in different regimes. So, a momentum strategy may not work properly when market is not trending.

At the same time, a mean reversion strategy may not perform the best when market is trending. Hence, you can combine momentum with mean reversion strategies to generate consistent returns. Here, you can allocate some of the capital to momentum strategies and some to mean reversion strategies based on expected market situation.

Similarly, you can allocate the capital to different strategies depending on the situations such as market volatility current and expected. Also, you can trade in a variety of trade-able items such as Forex currency , equity shares, commodities etc. to create a portfolio and apply different strategies as mentioned above.

Trading strategy creation is an important step during automated execution of trades. For more information, see Interpolation of Primitive Variables. The IsA schema to which a prim subscribes is determined by the authored value of its typeName metadata, from which it follows that a prim can subscribe to at most one IsA schema - unlike API schemas , to which a prim can subscribe many.

UsdGeomMesh , however, is a concrete IsA schema, since it has a Define method and prims can possess the typeName Mesh. IsA schemas can provide fallback values for the properties they define, which will be reflected at runtime in the prim definition. IsA schemas can be generated using the USD schema generation tools , but they can also be created manually. Kind is a reserved, prim-level metadatum whose authored value is a simple string token, but whose interpretation is backed by an extensible hierarchical typing-system managed by the KindRegistry singleton.

model - base class for all model kinds. group - models that simply group other models. assembly - an important group model, often a published asset or reference to a published asset. A Layer is the atomic persistent container of scene description for USD. A layer contains zero or more PrimSpecs , that in turn describe Property and Metadata values. Each layer possesses an identifier that can be used to construct references to the layer from other layers.

This is not only how USD supports direct consumption of Alembic. Data authored to layers by applications or scripts will remain in memory until SdfLayer::Save is called on the layer. usd file extension. Calling layer. Save periodically: doing so will flush all of the heavy property-value data from memory into the file, while leaving the file open and available for continued writing of data.

Composition arcs such as References and SubLayers can include an offset and scaling of time to be applied during attribute value resolution for all data composed from the target layer. We call this a Layer Offset , embodied in SdfLayerOffset. Layer offsets compose, so that if A references B with a time-scale of 2. When an arc has both an offset and scale applied, the referenced animation is first scaled, then offset as it is brought into the referencing layer. So, in the following example, a timeSample at timeCode 12 in the file someAnimation.

Layer offsets cannot themselves vary over time. If a consuming context requires variable retiming of referenced data, it can use the more powerful and somewhat more costly Value Clips feature. LayerStacks are the keystone to understanding composition in USD. The definition of a LayerStack is simply:. LayerStack: The ordered set of layers resulting from the recursive gathering of all SubLayers of a Layer , plus the layer itself as first and strongest.

When a layer references or payloads or sub-layers another layer, it is targeting and therefore composing not just the data in the single layer, but all the data in strength-order in the LayerStack rooted at the targeted layer. Many of the composition arcs as well as relationships describe not just a single target, but an orderable list of targets , that will be processed in order according to the type of the arc.

References can be list edited among the layers of a LayerStack. This can be a powerful method of non-destructively changing the large-scale structure of a scene as it flows down the pipeline. For example, we might have a generic version of a special effect added into a scene at the sequence level:. Now, at the shot-level, we have a shotFX. usd layer that participates in the same LayerStack as sequenceFX.

usd because one of the shot layers SubLayers in the sequence. usd layer, which in turn SubLayers the above sequenceFX.

usd layer. In this particular shot, we need to replace the generic turbulence effect with a different one, which may have completely different prims in it. usd, but not turbulence. In this second example we have also shown that the operand of list-editing operations can be a list that can contain multiple targets. List editing is a feature to which some array-valued data elements in USD can subscribe, that allows the array-type element to be non-destructively, sparsely modified across composition arcs.

It would be very expensive and difficult to reason about list editable elements that are also time-varying, so Attributes can never be list editable. When an element is list editable, instead of only being able to assign an explicit value to it, you can also, in any stronger layer:.

append another value or values to the back of the resolved list; if the values already appear in the resolved list, they will be reshuffled to the back. delete a value or values from the resolved list. prepend another value or values on the front of the resolved list; if the values already appear in the resolved list, they will be shuffled to the front. A prepended composition arc in a weaker layer of a LayerStack will still be stronger than any arcs of the same type that are appended from stronger layers.

This causes the resolved list to be reset to the provided value or values, ignoring all list ops from weaker layers. Also, in the usda text syntax, any operation can assign either a single value without the square-bracket list delimiters, or a sequence of values inside square brackets.

See LayerStack for an example of list editing, as applied to references. See also the FAQ on deleting items with list ops: When can you delete a reference or other deletable thing? LIVRPS is an acronym for Local, Inherits, VariantSets, References, Payload, Specializes , and is the fundamental rubric for understanding how opinions and namespace compose in USD.

LIVRPS describes the strength ordering in which the various composition arcs combine, within each LayerStack. Iterate through all the layers in the local LayerStack looking for opinions on the PrimSpec at path in each layer - recall that according to the definition of LayerStack, this is where the effect of direct opinions in all SubLayers of the root layer of the LayerStack will be consulted.

If no opinion is found, then…. Resolve the Inherits affecting the prim at path , and iterate through the resulting targets. Apply the resolved variant selections to all VariantSets that affect the PrimSpec at path in the LayerStack, and iterate through the selected Variants on each VariantSet.

Resolve the References affecting the prim at path , and iterate through the resulting targets. Resolve the Payload arcs affecting the prim at path ; if path has been loaded on the stage, iterate through the resulting targets just as we would references from step 4. It may sound like a great deal of work to need to perform for every value lookup, and it absolutely would be if we followed all the steps as described above, during value resolution.

The algorithm for computing the namespace of the stage i. what prims are present and where are slightly more involved, but still follows the LIVRPS recipe. Wherever a Stage contains at least one Payload payloads can be list-edited and chained , the client has the ability to Load compose all the scene description targeted by the Payload, or to Unload the Payloads and all their scene description, recomposing all prims beneath the payloaded-prim, recursively unloading their payloads, should they possess any.

For more information, see Working Set Management in the USD Manual. USD allows the construction of highly referenced and layered scenes that assemble files from many different sources, which may resolve differently in different contexts for example, your asset resolver may apply external state to select between multiple versions of an asset. UsdUtilsCreateNewUsdzPackage does this for you, although we have not yet exposed the ability to just localize yet, but we hope to eventually.

Metadata are extensible, however adding a new, named piece of metadata requires a change to a configuration file to do so, because the software wants to know, definitively, what the datatype of the metadatum should be.

USD provides a special, dictionary-valued metadatum called customData that provides a place to put user metadata without needing to touch any configuration files. For more information on the allowed types for metadata and how to add new metadata to the system, please see the discussion of metadata in the API manual. Model is a scenegraph annotation ascribable to prims by setting their kind metadata.

See also Model Hierarchy. The model hierarchy defines a contiguous set of prims descending from a root prim on a stage, all of which are models. Model hierarchy is an index of the scene that is, strictly, a prefix , of the entire scene. The member prims must adhere to the following three rules:.

Only group model prims can have other model children assembly is a kind of group. A prim can only be a model if its parent prim is also a group model - except for the root model prim.

This implies that component models cannot have model children. Reasoning about referencing structure can get complicated very quickly and necessitate introducing fragile conventions. Namespace is simply the term USD uses to describe the set of prim paths that provide the identities for prims on a Stage , or PrimSpecs in a Layer. Opinions are the atomic elements that participate in Value Resolution in USD.

Each time you author a value for a Metadatum, Attribute, or Relationship, you are expressing an opinion for that object in a PrimSpec in a particular Layer. On a composed Stage, any object may be affected by multiple opinions from different layers; the ordering of these opinions is determined by the LIVRPS strength ordering. Over is one of the three possible specifiers a prim and also a PrimSpec can possess.

When an application exports sparse overrides into a layer that sits on top of an existing composition, it is common to see deep nesting of overs. A path is a location in namespace. In USD text syntax and documentation , paths are enclosed in angle-brackets, as found in the authored targets for references , payloads , inherits , specializes , and relationships. USD assigns paths to all elements of scene description other than metadata, and the concrete embodiment of a path, SdfPath , serves in the API as a compact, thread-safe, key by which to fetch and store scene description, both within a Layer, and composed on a Stage.

The SdfPath syntax allows for recording paths to different kinds of scene description. For example:. visibility names the property visibility on the prim Grandchild.

Path translation is applied during such queries as finding a prim and fetching the targets of a relationship or connection , and inverse path translation is performed by the active Edit Target whenever you author to a stage.

usd into a shot. gprims , we will get back:. Deleting the target in shot. usd might look like this:. We mentioned above that path translation also operates in the opposite direction when you use Edit Targets to send your relationship or connection edits across a composition arc, because it follows that every encoded path must be in the namespace of the PrimSpec on which it is recorded.

For example, if we were working with the same shot. usd that looks like:. A Payload is a composition arc that is a special kind of a Reference. It is different from references primarily in two ways:. The targets of References are always consumed greedily by the indexing algorithm that is used to open and build a Stage. When a Stage is opened with UsdStage::InitialLoadSet::LoadNone specified, Payload arcs are recorded, but not traversed.

Payloads are weaker than references , so, for a particular prim within any given LayerStack , all direct references will be stronger than all direct payloads. See the performance note on packaging assets with payloads. Prims, along with their associated, computed indices , are the only persistent scenegraph objects that a Stage retains in memory, and the API for interacting with prims is provided by the UsdPrim class. A prim definition is the set of built-in properties and metadata that a prim gains from a combination of the IsA schema determined from its typeName and its applied API schemas.

The API for prim definitions are provided by the UsdPrimDefinition class. Each composed Prim on a Stage is the result of potentially many PrimSpecs each contributing their own scene description to a composite result. Similarly to a composed prim, a PrimSpec is a container for property data and nested PrimSpecs.

Importantly, composition arcs can only be applied on PrimSpecs, and those arcs that specify targets are targeting other PrimSpecs. In USD, you create and retrieve primvars using the UsdGeomImageable schema, and interact with the special primvar encoding using the UsdGeomPrimvar schema.

Primvars define a value that can vary across the primitive on which they are defined, via prescribed interpolation rules. Different renderers may communicate the variables to the shaders using different mechanisms over which Usd has no control; Primvars simply provide the classification that any renderer should use to locate potential overrides. Properties are the other kind of namespace object in USD Prims being the first.

There are two types of Property: Attribute and Relationship. All properties can be ordered within their containing Prim and are otherwise enumerated in dictionary order via UsdPrim::SetPropertyOrder , and can host Metadata. Here are some examples of namespaced properties from usd schemas:.

Just as PrimSpecs contain data for a prim within a layer, PropertySpecs contain the data for a property within a layer. A PropertyStack is a list of PropertySpecs that contribute a default or timeSample for Attributes or target for relationships , or any piece of metadata , for a given property. A PropertyStack does not contain the proper time-offsets that must be applied to the PrimSpecs to retrieve the correct timeSample when there are authored Layer Offsets on references , subLayers , or clips.

If your goal is to optimize repeated value resolutions on attributes, retain a UsdAttributeQuery instead, which is designed for exactly this purpose. Proxy is a highly overloaded term in computer graphics… but so were all the alternatives we considered for the same concept in USD.

The idea behind this pairing is that the proxy provides a set of gprims that are lightweight to read and draw, and provide an idea of what the full render geometry will look like, at much cheaper cost. The answer is twofold:. Most clients of USD in our pipeline place a high value on bringing up a complex scene for inspection and introspection as quickly as possible.

It is a fairly lightweight operation to instruct the renderer to ignore the proxies and image the full render geometry, when that is required. See Namespace for further details. Purpose is a builtin attribute of the UsdGeomImageable schema, and is a concept we have found useful in our pipeline for classifying geometry into categories that can each be independently included or excluded from traversals of prims on a stage, such as rendering or bounding-box computation traversals.

For a discussion of the motivation for purpose , see Proxy. After SubLayers , References are the next most-basic and most-important composition arc. Because a PrimSpec can apply an entire list of References, References can be used to achieve a similar kind of layering of data, when one knows exactly which prims need to be layered and with some differences in how the participating opinions will be resolved.

Following is a simple example of referencing, with overrides. We start with a trivial model asset, Marble. Note that, for brevity, we are eliding some of the key data usually found in published assets such as AssetInfo , shading of any kind, Inherits , a Payload , detailed model substructure.

Now we want to create a collection of marbles, by referencing the Marble asset multiple times, and overriding some of the referenced properties to make each instance unique. In the composed namespace, the prim name Marble is gone, since the references allowed us to perform a prim name-change on the prim targeted by the reference. This is a key feature of references, since without it, we would be unable to reference the same asset more than once within any given prim scoping, because sibling prims must be uniquely named to form a proper namespace.

usd , the file Marble. usd was only opened once and shared by both references. For deeper sharing of referenced assets, in which the prims themselves are also shared, see Instancing.

References can apply a Layer Offset to offset and scale the time-varying data contained in the referenced layer s. References can target any prim in a LayerStack , excepting ancestors of the prim containing the reference, if the reference is an internal reference targeting the same LayerStack in which the reference is authored.

When targeting sub-root prims, however, there is the potential for surprising behavior unless you are aware of and understand the ramifications. One such ramification is that if the targeted sub-root prim has an ancestor prim that contains a VariantSet , the referencer will have no ability to express a selection for that VariantSet.

For a more complete discussion of the ramifications of referencing sub-root prims, see the UsdReferences class documentation. See List Editing for the rules by which references can be combined within a LayerStack. Relationships can have multiple targets, as, for instance, the relationships in a UsdCollectionAPI target all of the objects that belong to the named collection; therefore, relationships are List Edited. Now, because each marble in the MarbleCollection.

usd scene has its own copy of the GlassMaterial prim, we expect that when we:. as the result, even though that was not the authored value in Marble. The prims declared in root layers are the only ones locatable using the same paths that identify composed prims on the Stage.

Currently, an Edit Target can only target PrimSpecs in the root LayerStack, although we hope to relax that restriction eventually. It is the layers of the root LayerStack that are the most useful in facilitating shared workflows using USD. USD defines a schema as an object whose purpose is to author and retrieve structured data from some UsdObject. Schemas are lightweight objects we create to wrap a UsdObject , as and when needed, to robustly interrogate and author scene description.

Specializes is a composition arc that allows a specialized prim to be continuously refined from a base prim, through unlimited levels of referencing. A specialized prim will contain all of the scene description contained in the base prim it specializes including the entire namespace hierarchy rooted at the base prim , but any opinion expressed directly on the specialized prim will always be stronger than any opinion expressed on the base prim, in any referencing context.

Let us examine an example inspired by the first uses of the specializes arc at Pixar: specializing materials in our shading schema. For brevity, we focus on one particular specialization, a corroded metal; we also leave out many of the schema details of how materials and their shaders interact. The above example is not realistic at all regarding how you would actually design a Metal or CorrodedMetal material! Specializes can target any prim on the stage that is neither an ancestor nor descendant of the specializing prim.

In the example above, replacing the specializes with inherits will produce the same composed result - try it! In the sameRobot.

The unique behavior of specializes only becomes evident, however, in a referencing context, so let us try one. If you examine the flattened RobotScene.

This also demonstrates the difference between specializes and inherits : if you change the specializes arc to inherits in Robot. The specializes behavior is desirable in this context of building up many unique refinements of something whose base properties we may want to continue to update as assets travel down the pipeline, but without changing anything that makes the refinements unique.

What if we do want to broadcast an edit on the Metal material to all of the Materials that specialize it? class - prims from which other prims inherit. The most common, default traversals, which are meant to be used for rendering and other common scenegraph processing, will visit only defined , non-abstract prims.

A stage always presents the composed view of the scene description that backs it. Mutated stage layers can be collectively saved to backing-store using UsdStage::Save. Most consumption of USD data follows the pattern of traversing an open stage, processing prims one at a time, or several in parallel. Given a UsdPrim p , one can fetch its direct children on the Stage using p. One can also fetch a UsdPrimSubtreeRange directly from a prim via p. Additionally, and more commonly used in USD core code, one can traverse the subtree rooted at a prim by creating a UsdPrimRange , which allows for depth-first iteration with the ability to prune subtrees at any point during the iteration.

Some examples are whether a prim is defined, or active, or loaded, etc. For a full list of the possible prim flags and examples of how they can be logically combined, see prim predicate flags. The remaining methods e. GetChildren all use a predefined Default Predicate that serves a common traversal pattern. An exporter may choose to identify articulation points in a complicated model by labeling such prims as subcomponents for example, the DoorAssembly Xform inside an architectural model.

SubLayers is the composition arc used to construct LayerStacks. As an example, here is one possible combination of USD layers that could have been the source for the example in the LayerStack entry, and also demonstrate how SubLayers supports nested LayerStacks:. Note that SubLayers can specify Layer Offsets to offset and scale time-varying data contained in the sub-layer s. TimeCodes are the unit-less time ordinate in USD.

TimeSamples as source for Value Resolution. USD API sometimes refers to just the ordinate of a time-varying value as a TimeSample; for example, UsdAttribute::GetTimeSamples and UsdAttribute::GetTimeSamplesInInterval return a simple vector of time ordinates at which samples may be resolved on the attribute.

We plan to create a UsdUserPropertiesAPI schema to aid in authoring and enumerating user properties, but creating a property in the userProperties: namespace using UsdPrim::CreateAttribute not all importers may handle custom relationships properly is sufficient. Value Clips are a feature that allows one to partition varying attribute timeSample overrides into multiple files, and combine them in a manner similar to how non-linear video editing tools allow one to combine video clips.

Clips are especially useful for solving two important problems in computer graphics production pipelines:. Crowd animators will often create animation clips that can apply to many background characters, and be sequenced and cycled to generate a large variety of animation.

USD clips provide the ability to encode the sequencing and non-uniform time-mapping of baked animation clips that this task requires. USD Clips make it possible to stitch all of these files together into a continuous even though the data may itself be topologically varying over time animation, without needing to move, merge, or perturb the files that the simulator produced. The USD toolset includes a utility usdstitchclips that efficiently assembles a sequence of file-per-frame layers into a Value Clips representation.

The key advantage of the clips feature is that the resulting resolved animation on a UsdStage is indistinguishable from data collected or aggregated into a single layer. In other words, consuming clients can be completely unaware of the existence of clips: there is no special schema or API required to access the data.

The disadvantages of using clips are:. Encoding clips on a stage is more complicated than simply recording samples on attributes, or adding references see UsdClipsAPI for details on encoding.

For more information on value clip behavior and how clips are encoded, see Sequenceable, Re-timeable Animated Value Clips in the USD Manual. Even though value resolution is the act of composing potentially many pieces of data together to produce a single value, we distinguish value resolution from composition because understanding the differences between the two aids in effective construction and use of USD:.

The USD core does not, however, pre-compute or cache any per-composed-property information, which is a principal design decision aimed at keeping latency low for random-access to composed data, and keeping the minimal memory footprint for USD low. Instead, for attribute value resolution, the USD core provides opt-in facilities such as UsdAttributeQuery and UsdResolveInfo , objects a client can construct and retain themselves that cache information that can make repeated value queries faster.

Composition is internally multi-threaded, value resolution is meant to be client multi-threaded. Composition of a large stage can be a big computation, and USD strives to effectively, internally multi-thread the computation; therefore clients should realize they are unlikely to gain performance from opening multiple stages simultaneously in different threads.

Composition rules vary by composition arc, value resolution rules vary by metadatum. Value resolution simply consumes the ordered strong-to-weak list of contributing sites, and is otherwise insensitive to the particular set of composition arcs that produced that list; but how the data in those sites is combined depends on the particular metadatum being resolved.

The basic rule for the metadata value resolution provided by UsdObject::GetMetadata is: strongest opinion wins. Certain metadata such as prim specifier , attribute typeName, and several others have special resolution rules; the only one we will discuss here is dictionary-valued metadata , because it is user-facing, as, for example, the customData dictionary authorable on any prim or property.

The rules for how the opinions combine in weak-to-strong order are contained inside SdfListOp::ApplyOperations. Value resolution for attributes, as performed by UsdAttribute::Get , is unique in three ways:.

UsdAttribute::Get is a function of time, so all queries except those evaluated at UsdTimeCode::Default are affected by time-scaling operators such as Layer Offsets. If the requested time ordinate falls between two samples, and the stage is configured for linear interpolation which it is by default , then we will attempt to apply linear interpolation of the bracketing timeSamples , before falling back to holding the earlier of the two timeSamples.

For attributes, however, we must examine three possible sources for a value for each site, before moving on to the next site in strong-to-weak order:. Value Clips that are anchored at the site or an ancestor site in namespace. If no clips are found, or if clips do not provide a value for the attribute, then…. TimeSamples authored directly at the site. If there are no TimeSamples, then….

Default Value authored directly at the site. The default UsdTimeCode value for UsdAttribute::Get is UsdTimeCode::Default , which is almost always a poor choice when resolving values on a stage that contains animation.

When writing code that extracts attribute values from a stage, if the codesite is not provided an explicit querying time, you should use UsdTimeCode::EarliestTime , which will ensure that if there is any timeSample authored for the attribute, it will provide the value, rather than the default , which is all that is consulted when UsdTimeCode::Default is the given time ordinate.

Variability is not consulted or consumed by the core during authoring or value resolution, in order to keep those operations fast. It appears in schema-generated documentation , and can be used for validation by higher-level authoring code, and as a hint to clients that the value is not expected to change over time.

See also UsdAttribute::GetVariability. A variant represents a single, named variation of a VariantSet ; each VariantSet can have zero or a single variant selection expressed in scene description or as a fallback in a plugin or specified by an application , if no selection was authored. A VariantSet is a composition arc that allows a content creator to package a discrete set of alternatives, between which a downstream consumer is able to non-destructively switch, or augment.

Each Variant of a VariantSet encapsulates a tree of scene description that will be composed onto the prim on which the VariantSet is defined, when the Variant is selected. VariantSet names must be legal USD identifiers. Here is an example of a very simple VariantSet:. usd in usdview , nothing will be drawn! This is because we have not specified a selection for shapeVariant see Variant for what a selection looks like , which is always a valid thing to do, and simply means none of the variants contained in the VariantSet will be applied.

Variants are not just about overrides! In simpleVariantSet. usd , we are creating a differently-named-and-typed child prim in each variant. Each variant can in fact create an entire subtree of prims that will be combined on top of any referenced scene description , as well as apply overrides.

A prim can have an unlimited number of VariantSets declared directly on itself; the VariantSets can be ordered initial order is authored order as part of their List Editing nature, and the final order of the VariantSets provides their relative strength with respect to each other, should their opinions overlap. Each Variant can contain arbitrary scene description including the introduction of additional composition arcs. This gives us great flexibility in building up variations out of existing, modular pieces that can be referenced , inherited , etc.

Because VariantSets are just composition arcs, this implies that VariantSets can be directly nested inside each other , by allowing a Variant of a VariantSet to introduce a new VariantSet. This ability facilitates the use of VariantSets to provide as-you-go asset version control, which can be especially useful, for example, in refining simulations or special effects.

One shot , however, requires a closeup, necessitating a higher resolution simulation. If the sequence-level simulation was placed inside a version VariantSet, in a SequenceBase Variant, then the special shot can, in a layer stronger, simply introduce a new CloseupShot Variant to the version VariantSet. In that shot, both variants will be available and selectable. In other shots, only the SequenceBase Variant will be available.

VariantSets allow optimal scattering of instancing variation , because for any given instanceable prim , its instancing key, which is what we use to decide which scene-level instances will share the same prototype, takes the variant selections on the prim into account.

So, if you build and publish an asset with one-or-more VariantSets on its root prim, an environment-building tool can confidently add many instances of the asset to a scene, scattering variation by scattering variant-selections, and the USD core will ensure only as many unique prototypes are compose as there are unique combinations of variant-selections.

As mentioned above, VariantSets can be nested directly inside each other, on the same prim. VariantSet nesting in USD can be accomplished straightforwardly by nesting the use of UsdEditContext objects in code. Following is a small python program that nests two VariantSets, demonstrating that the contents of the inner VariantSet can vary from Variant to Variant of the outer VariantSet. Try loading the resulting nestedVariants.

usd in usdview. This is not an error condition, and the result is simply that no jobVariant is applied. Visibility can take on two possible token string values:. Visibility may be animated, allowing a sub-tree of geometry to be renderable for some segment of a shot, and absent from others; unlike the action of deactivating geometry prims, invisible geometry is still available for inspection, for positioning, for defining volumes, etc.

Universal Scene Description. What programming languages are supported? So what file formats does USD support? What file format is my. usd file? What character encoding does. usda support? How can I convert USD files between binary and text? What data types are supported? What does a USD file look like? Subtler Aspects of Scene Description and Composition I have some layers I want to combine: Should I use SubLayers or References?

When can you delete a reference or other deletable thing? Third Party Plugins RenderMan USD Imaging Plugin Configuration Developer Alembic USD Plugin Known Limitations Collaborate Source Code GitHub usd-interest Group Contributing Contributor License Agreement Coding Conventions Pull Request Guidelines Git Workflow Contributors Press Open Source Release Open Source Announcement. USD Terms and Concepts  USD introduces quite a few terms and concepts, some of which, unavoidably, already have different meanings in other contexts, so it can be quite daunting to make sense of all of our documentation and code until you have become fully indoctrinated.

A footnote in Microsoft's submission opens in new tab to the UK's Competition and Markets Authority CMA has let slip the reason behind Call of Duty's absence from the Xbox Game Pass library: Sony and Activision Blizzard have a deal that restricts the games' presence on the service.

The footnote appears in a section detailing the potential benefits to consumers from Microsoft's point of view of the Activision Blizzard catalogue coming to Game Pass.

What existing contractual obligations are those? Why, ones like the "agreement between Activision Blizzard and Sony," that places "restrictions on the ability of Activision Blizzard to place COD titles on Game Pass for a number of years". It was apparently these kinds of agreements that Xbox's Phil Spencer had in mind opens in new tab when he spoke to Sony bosses in January and confirmed Microsoft's "intent to honor all existing agreements upon acquisition of Activision Blizzard".

Unfortunately, the footnote ends there, so there's not much in the way of detail about what these restrictions are or how long they'd remain in effect in a potential post-acquisition world. Given COD's continued non-appearance on Game Pass, you've got to imagine the restrictions are fairly significant if they're not an outright block on COD coming to the service.

Either way, the simple fact that Microsoft is apparently willing to maintain any restrictions on its own ability to put first-party games on Game Pass is rather remarkable, given that making Game Pass more appealing is one of the reasons for its acquisition spree. The irony of Sony making deals like this one while fretting about COD's future on PlayStation probably isn't lost on Microsoft's lawyers, which is no doubt part of why they brought it up to the CMA. While it's absolutely reasonable to worry about a world in which more and more properties are concentrated in the hands of singular, giant megacorps, it does look a bit odd if you're complaining about losing access to games while stopping them from joining competing services.

We'll find out if the CMA agrees when it completes its in-depth, "Phase 2" investigation opens in new tab into the Activision Blizzard acquisition, which is some way off yet. For now, we'll have to content ourselves with poring over these kinds of corporate submissions for more interesting tidbits like this one.

So far, we've already learned that Microsoft privately has a gloomy forecast for the future of cloud gaming opens in new tab , and that the company thinks Sony shouldn't worry so much since, hey, future COD games might be as underwhelming as Vanguard opens in new tab. Who knows what we'll learn next? Sign up to get the best content of the week, and great gaming deals, as picked by the editors.

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All Collectible Firearms. By java cylinder and 2 invulnerable save; There are three types of API schema: non-applied, single-apply, and multiple-apply. If there are no TimeSamples, then… Default Value authored directly at the site. Store Vs. The irony of Sony making deals like this one while fretting about COD's future on PlayStation probably isn't lost on Microsoft's lawyers, which is no doubt part of why they brought it up to the CMA.

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