Dtw algo (#9)
* feat: implement Dynamic Time Warping (DTW) functionality - Added DTW distance computation and optimal warping path functions in Rust. - Introduced corresponding Python bindings for DTW, DTW_DISTANCE, and BATCH_DTW. - Enhanced WASM support with a new dtw_distance function. - Included comprehensive unit tests for DTW functionality, validating against the dtaidistance library and ensuring mathematical properties. * chore: update ferro-ta version to 1.1.4 - Bumped version number of ferro-ta to 1.1.4 in uv.lock and Cargo.lock files. - Ensured consistency across package dependencies for the updated version.
This commit is contained in:
Pratik Bhadane
Generated
+26
-26
@@ -34,9 +34,9 @@ checksum = "940b3a0ca603d1eade50a4846a2afffd5ef57a9feac2c0e2ec2e14f9ead76000"
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[[package]]
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name = "arc-swap"
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version = "1.9.0"
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version = "1.9.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a07d1f37ff60921c83bdfc7407723bdefe89b44b98a9b772f225c8f9d67141a6"
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checksum = "6a3a1fd6f75306b68087b831f025c712524bcb19aad54e557b1129cfa0a2b207"
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dependencies = [
|
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"rustversion",
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]
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@@ -67,9 +67,9 @@ checksum = "37b2a672a2cb129a2e41c10b1224bb368f9f37a2b16b612598138befd7b37eb5"
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[[package]]
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name = "cc"
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version = "1.2.57"
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version = "1.2.59"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "7a0dd1ca384932ff3641c8718a02769f1698e7563dc6974ffd03346116310423"
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checksum = "b7a4d3ec6524d28a329fc53654bbadc9bdd7b0431f5d65f1a56ffb28a1ee5283"
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dependencies = [
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"find-msvc-tools",
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"shlex",
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@@ -207,7 +207,7 @@ checksum = "48c757948c5ede0e46177b7add2e67155f70e33c07fea8284df6576da70b3719"
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[[package]]
|
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name = "ferro_ta"
|
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version = "1.1.3"
|
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version = "1.1.4"
|
||||
dependencies = [
|
||||
"criterion",
|
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"ferro_ta_core",
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@@ -222,7 +222,7 @@ dependencies = [
|
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[[package]]
|
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name = "ferro_ta_core"
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version = "1.1.3"
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version = "1.1.4"
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dependencies = [
|
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"criterion",
|
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"serde",
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@@ -279,9 +279,9 @@ checksum = "8f42a60cbdf9a97f5d2305f08a87dc4e09308d1276d28c869c684d7777685682"
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[[package]]
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name = "js-sys"
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version = "0.3.91"
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version = "0.3.94"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "b49715b7073f385ba4bc528e5747d02e66cb39c6146efb66b781f131f0fb399c"
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checksum = "2e04e2ef80ce82e13552136fabeef8a5ed1f985a96805761cbb9a2c34e7664d9"
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dependencies = [
|
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"once_cell",
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"wasm-bindgen",
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@@ -289,9 +289,9 @@ dependencies = [
|
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[[package]]
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name = "libc"
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version = "0.2.183"
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version = "0.2.184"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "b5b646652bf6661599e1da8901b3b9522896f01e736bad5f723fe7a3a27f899d"
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checksum = "48f5d2a454e16a5ea0f4ced81bd44e4cfc7bd3a507b61887c99fd3538b28e4af"
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[[package]]
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name = "log"
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@@ -595,9 +595,9 @@ checksum = "dc897dd8d9e8bd1ed8cdad82b5966c3e0ecae09fb1907d58efaa013543185d0a"
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[[package]]
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name = "rustc-hash"
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version = "2.1.1"
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version = "2.1.2"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "357703d41365b4b27c590e3ed91eabb1b663f07c4c084095e60cbed4362dff0d"
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checksum = "94300abf3f1ae2e2b8ffb7b58043de3d399c73fa6f4b73826402a5c457614dbe"
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[[package]]
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name = "rustversion"
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@@ -729,9 +729,9 @@ dependencies = [
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[[package]]
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name = "wasm-bindgen"
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version = "0.2.114"
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version = "0.2.117"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "6532f9a5c1ece3798cb1c2cfdba640b9b3ba884f5db45973a6f442510a87d38e"
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checksum = "0551fc1bb415591e3372d0bc4780db7e587d84e2a7e79da121051c5c4b89d0b0"
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dependencies = [
|
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"cfg-if",
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"once_cell",
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@@ -742,9 +742,9 @@ dependencies = [
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[[package]]
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name = "wasm-bindgen-macro"
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version = "0.2.114"
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version = "0.2.117"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "18a2d50fcf105fb33bb15f00e7a77b772945a2ee45dcf454961fd843e74c18e6"
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checksum = "7fbdf9a35adf44786aecd5ff89b4563a90325f9da0923236f6104e603c7e86be"
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dependencies = [
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"quote",
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"wasm-bindgen-macro-support",
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@@ -752,9 +752,9 @@ dependencies = [
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[[package]]
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name = "wasm-bindgen-macro-support"
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version = "0.2.114"
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version = "0.2.117"
|
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "03ce4caeaac547cdf713d280eda22a730824dd11e6b8c3ca9e42247b25c631e3"
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checksum = "dca9693ef2bab6d4e6707234500350d8dad079eb508dca05530c85dc3a529ff2"
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dependencies = [
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"bumpalo",
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"proc-macro2",
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@@ -765,18 +765,18 @@ dependencies = [
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[[package]]
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name = "wasm-bindgen-shared"
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version = "0.2.114"
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version = "0.2.117"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "75a326b8c223ee17883a4251907455a2431acc2791c98c26279376490c378c16"
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checksum = "39129a682a6d2d841b6c429d0c51e5cb0ed1a03829d8b3d1e69a011e62cb3d3b"
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dependencies = [
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"unicode-ident",
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]
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[[package]]
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name = "web-sys"
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version = "0.3.91"
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version = "0.3.94"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "854ba17bb104abfb26ba36da9729addc7ce7f06f5c0f90f3c391f8461cca21f9"
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checksum = "cd70027e39b12f0849461e08ffc50b9cd7688d942c1c8e3c7b22273236b4dd0a"
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dependencies = [
|
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"js-sys",
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"wasm-bindgen",
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@@ -840,18 +840,18 @@ dependencies = [
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[[package]]
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name = "zerocopy"
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version = "0.8.47"
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version = "0.8.48"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "efbb2a062be311f2ba113ce66f697a4dc589f85e78a4aea276200804cea0ed87"
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checksum = "eed437bf9d6692032087e337407a86f04cd8d6a16a37199ed57949d415bd68e9"
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dependencies = [
|
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"zerocopy-derive",
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]
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[[package]]
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name = "zerocopy-derive"
|
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version = "0.8.47"
|
||||
version = "0.8.48"
|
||||
source = "registry+https://github.com/rust-lang/crates.io-index"
|
||||
checksum = "0e8bc7269b54418e7aeeef514aa68f8690b8c0489a06b0136e5f57c4c5ccab89"
|
||||
checksum = "70e3cd084b1788766f53af483dd21f93881ff30d7320490ec3ef7526d203bad4"
|
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dependencies = [
|
||||
"proc-macro2",
|
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"quote",
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+2
-2
@@ -5,7 +5,7 @@ resolver = "2"
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[package]
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name = "ferro_ta"
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version = "1.1.3"
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version = "1.1.4"
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edition = "2021"
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description = "Rust-powered Python technical analysis library with a TA-Lib-compatible API"
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license = "MIT"
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@@ -30,7 +30,7 @@ ndarray = "0.16"
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rayon = "1.10"
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log = "0.4"
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pyo3-log = "0.12"
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ferro_ta_core = { path = "crates/ferro_ta_core", version = "1.1.3", features = ["serde"] }
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ferro_ta_core = { path = "crates/ferro_ta_core", version = "1.1.4", features = ["serde"] }
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[dev-dependencies]
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criterion = { version = "0.8", features = ["html_reports"] }
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+1
-1
@@ -1,5 +1,5 @@
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{% set name = "ferro-ta" %}
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{% set version = "1.1.3" %}
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{% set version = "1.1.4" %}
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package:
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name: {{ name|lower }}
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@@ -1,6 +1,6 @@
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[package]
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name = "ferro_ta_core"
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version = "1.1.3"
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version = "1.1.4"
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||||
edition = "2021"
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description = "Pure Rust core indicator library — no PyO3, no numpy dependency"
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license = "MIT"
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@@ -13,7 +13,7 @@ PyO3, NumPy, or Python runtime dependency, which makes it a good fit for:
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```toml
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[dependencies]
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ferro_ta_core = "1.1.3"
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ferro_ta_core = "1.1.4"
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```
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## Design
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@@ -247,6 +247,118 @@ pub fn correl(real0: &[f64], real1: &[f64], timeperiod: usize) -> Vec<f64> {
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result
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}
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// ---------------------------------------------------------------------------
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// Dynamic Time Warping (DTW)
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// ---------------------------------------------------------------------------
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/// Internal helper: build the full DTW accumulated-cost matrix.
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///
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/// Local cost: `|s1[i] - s2[j]|` (Euclidean / L1 for 1-D series).
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/// This matches the convention used by `dtaidistance.dtw.distance()`.
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///
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/// Out-of-band cells (Sakoe-Chiba constraint) are set to `f64::INFINITY`.
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fn dtw_matrix(s1: &[f64], s2: &[f64], window: Option<usize>) -> Vec<Vec<f64>> {
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let n = s1.len();
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let m = s2.len();
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let mut dp = vec![vec![f64::INFINITY; m]; n];
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for i in 0..n {
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// Window convention matches dtaidistance: window=w means |i-j| < w.
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// None = unconstrained (full matrix).
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let (j_lo, j_hi) = match window {
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None => (0, m),
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Some(w) => {
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let lo = i.saturating_sub(w.saturating_sub(1));
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let hi = i.saturating_add(w).min(m);
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(lo, hi)
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}
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};
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for j in j_lo..j_hi {
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// Squared Euclidean local cost — matches dtaidistance convention.
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// The final sqrt is applied only once at the top level (not per-step).
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let cost = (s1[i] - s2[j]).powi(2);
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let prev = if i == 0 && j == 0 {
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0.0
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} else if i == 0 {
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dp[0][j - 1]
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} else if j == 0 {
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dp[i - 1][0]
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} else {
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dp[i - 1][j - 1].min(dp[i - 1][j]).min(dp[i][j - 1])
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};
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dp[i][j] = cost + prev;
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}
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}
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dp
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}
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/// Compute the Dynamic Time Warping distance between two 1-D series.
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///
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/// Returns the accumulated Euclidean cost along the optimal warping path.
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/// Uses `|s1[i] - s2[j]|` as the local cost, matching `dtaidistance` convention.
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///
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/// # Arguments
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/// * `s1` - First time series.
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/// * `s2` - Second time series.
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/// * `window` - Optional Sakoe-Chiba band width. `None` = unconstrained.
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///
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/// Returns `f64::NAN` if either input is empty.
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pub fn dtw_distance(s1: &[f64], s2: &[f64], window: Option<usize>) -> f64 {
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if s1.is_empty() || s2.is_empty() {
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return f64::NAN;
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}
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let dp = dtw_matrix(s1, s2, window);
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// sqrt applied once at the end — matches dtaidistance.dtw.distance() convention.
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dp[s1.len() - 1][s2.len() - 1].sqrt()
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}
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/// Compute the DTW distance and the optimal warping path between two 1-D series.
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///
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/// The warping path is a `Vec<(usize, usize)>` of `(i, j)` index pairs,
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/// starting at `(0, 0)` and ending at `(n-1, m-1)`, monotonically non-decreasing.
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///
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/// # Arguments
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/// * `s1` - First time series.
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/// * `s2` - Second time series.
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/// * `window` - Optional Sakoe-Chiba band width. `None` = unconstrained.
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///
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/// Returns `(f64::NAN, vec![])` if either input is empty.
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pub fn dtw_path(s1: &[f64], s2: &[f64], window: Option<usize>) -> (f64, Vec<(usize, usize)>) {
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if s1.is_empty() || s2.is_empty() {
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return (f64::NAN, vec![]);
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}
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let dp = dtw_matrix(s1, s2, window);
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let dist = dp[s1.len() - 1][s2.len() - 1].sqrt();
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// Backtrace from (n-1, m-1) to (0, 0)
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let mut path = Vec::new();
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let (mut i, mut j) = (s1.len() - 1, s2.len() - 1);
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path.push((i, j));
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while i > 0 || j > 0 {
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let (ni, nj) = match (i, j) {
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(0, _) => (0, j - 1),
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(_, 0) => (i - 1, 0),
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_ => {
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let diag = dp[i - 1][j - 1];
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let up = dp[i - 1][j];
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let left = dp[i][j - 1];
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let best = diag.min(up).min(left);
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if best == diag {
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(i - 1, j - 1)
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} else if best == up {
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(i - 1, j)
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} else {
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(i, j - 1)
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}
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}
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};
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i = ni;
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j = nj;
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path.push((i, j));
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}
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path.reverse();
|
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(dist, path)
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}
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#[cfg(test)]
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mod tests {
|
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use super::*;
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@@ -259,4 +371,92 @@ mod tests {
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assert!(v.abs() < 1e-10);
|
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}
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}
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#[test]
|
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fn dtw_identical_series_is_zero() {
|
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let a = vec![1.0, 2.0, 3.0, 4.0, 5.0];
|
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assert_eq!(dtw_distance(&a, &a, None), 0.0);
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}
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#[test]
|
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fn dtw_known_shifted_series() {
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// [0,1,2] vs [1,2,3]: DTW uses squared Euclidean local cost + final sqrt.
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// Optimal path (0,0)→(1,0)→(2,1)→(2,2), accumulated cost = 1+0+0+1 = 2, sqrt(2).
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// Matches dtaidistance.dtw.distance([0,1,2],[1,2,3]) = 1.4142...
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let a = vec![0.0, 1.0, 2.0];
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let b = vec![1.0, 2.0, 3.0];
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let expected = 2.0_f64.sqrt();
|
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let result = dtw_distance(&a, &b, None);
|
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assert!(
|
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(result - expected).abs() < 1e-12,
|
||||
"got {result}, expected {expected}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dtw_known_even_shift() {
|
||||
// [0,2,4] vs [1,3,5]: diagonal path, squared costs 1+1+1=3, sqrt(3).
|
||||
// Matches dtaidistance.dtw.distance([0,2,4],[1,3,5]) = 1.7320...
|
||||
let a = vec![0.0, 2.0, 4.0];
|
||||
let b = vec![1.0, 3.0, 5.0];
|
||||
let expected = 3.0_f64.sqrt();
|
||||
let result = dtw_distance(&a, &b, None);
|
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assert!(
|
||||
(result - expected).abs() < 1e-12,
|
||||
"got {result}, expected {expected}"
|
||||
);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dtw_single_element() {
|
||||
let a = vec![3.0];
|
||||
let b = vec![7.0];
|
||||
assert_eq!(dtw_distance(&a, &b, None), 4.0);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dtw_empty_returns_nan() {
|
||||
assert!(dtw_distance(&[], &[1.0, 2.0], None).is_nan());
|
||||
assert!(dtw_distance(&[1.0, 2.0], &[], None).is_nan());
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dtw_path_endpoints() {
|
||||
let a = vec![1.0, 2.0, 3.0, 4.0];
|
||||
let b = vec![1.5, 2.5, 3.5, 4.5];
|
||||
let (_, path) = dtw_path(&a, &b, None);
|
||||
assert_eq!(path.first(), Some(&(0, 0)));
|
||||
assert_eq!(path.last(), Some(&(3, 3)));
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dtw_path_is_monotone() {
|
||||
let a = vec![1.0, 3.0, 2.0, 5.0, 4.0];
|
||||
let b = vec![2.0, 1.0, 4.0, 3.0, 6.0];
|
||||
let (_, path) = dtw_path(&a, &b, None);
|
||||
for k in 1..path.len() {
|
||||
assert!(path[k].0 >= path[k - 1].0);
|
||||
assert!(path[k].1 >= path[k - 1].1);
|
||||
}
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dtw_path_distance_matches_distance_only() {
|
||||
let a = vec![1.0, 4.0, 2.0, 8.0, 3.0];
|
||||
let b = vec![2.0, 3.0, 7.0, 4.0, 5.0];
|
||||
let d1 = dtw_distance(&a, &b, None);
|
||||
let (d2, _) = dtw_path(&a, &b, None);
|
||||
assert!((d1 - d2).abs() < 1e-12);
|
||||
}
|
||||
|
||||
#[test]
|
||||
fn dtw_window_constrained_ge_unconstrained() {
|
||||
// window convention matches dtaidistance: Some(w) means |i-j| < w.
|
||||
// A narrow window restricts warping, so constrained distance >= unconstrained.
|
||||
let a: Vec<f64> = (0..20).map(|x| x as f64).collect();
|
||||
let b: Vec<f64> = (0..20).map(|x| x as f64 + 3.0).collect();
|
||||
let d_full = dtw_distance(&a, &b, None);
|
||||
let d_narrow = dtw_distance(&a, &b, Some(3));
|
||||
assert!(d_narrow >= d_full - 1e-12);
|
||||
}
|
||||
}
|
||||
|
||||
+63
-7
@@ -1,8 +1,8 @@
|
||||
{
|
||||
"surfaces": {
|
||||
"python": {
|
||||
"indicator_count": 208,
|
||||
"method_count": 464,
|
||||
"indicator_count": 211,
|
||||
"method_count": 467,
|
||||
"categories": [
|
||||
"aggregation",
|
||||
"alerts",
|
||||
@@ -131,6 +131,13 @@
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "BATCH_DTW",
|
||||
"category": "statistic",
|
||||
"module": "ferro_ta.indicators.statistic",
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "BBANDS",
|
||||
"category": "overlap",
|
||||
@@ -656,6 +663,20 @@
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "DTW",
|
||||
"category": "statistic",
|
||||
"module": "ferro_ta.indicators.statistic",
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "DTW_DISTANCE",
|
||||
"category": "statistic",
|
||||
"module": "ferro_ta.indicators.statistic",
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "DX",
|
||||
"category": "momentum",
|
||||
@@ -3283,6 +3304,13 @@
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "BATCH_DTW",
|
||||
"category": "statistic",
|
||||
"module": "ferro_ta.indicators.statistic",
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "BETA",
|
||||
"category": "statistic",
|
||||
@@ -3297,6 +3325,20 @@
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "DTW",
|
||||
"category": "statistic",
|
||||
"module": "ferro_ta.indicators.statistic",
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "DTW_DISTANCE",
|
||||
"category": "statistic",
|
||||
"module": "ferro_ta.indicators.statistic",
|
||||
"doc": "",
|
||||
"params": []
|
||||
},
|
||||
{
|
||||
"name": "LINEARREG",
|
||||
"category": "statistic",
|
||||
@@ -4735,7 +4777,7 @@
|
||||
]
|
||||
},
|
||||
"rust_core": {
|
||||
"public_function_count": 349,
|
||||
"public_function_count": 351,
|
||||
"functions": [
|
||||
{
|
||||
"module": "aggregation",
|
||||
@@ -6187,6 +6229,16 @@
|
||||
"function": "correl",
|
||||
"file": "statistic.rs"
|
||||
},
|
||||
{
|
||||
"module": "statistic",
|
||||
"function": "dtw_distance",
|
||||
"file": "statistic.rs"
|
||||
},
|
||||
{
|
||||
"module": "statistic",
|
||||
"function": "dtw_path",
|
||||
"file": "statistic.rs"
|
||||
},
|
||||
{
|
||||
"module": "statistic",
|
||||
"function": "linearreg",
|
||||
@@ -6485,7 +6537,7 @@
|
||||
]
|
||||
},
|
||||
"wasm_node": {
|
||||
"export_count": 221,
|
||||
"export_count": 222,
|
||||
"exports": [
|
||||
"ad",
|
||||
"adosc",
|
||||
@@ -6546,6 +6598,7 @@
|
||||
"digital_price",
|
||||
"donchian",
|
||||
"drawdown_series",
|
||||
"dtw_distance",
|
||||
"dx",
|
||||
"early_exercise_premium",
|
||||
"ema",
|
||||
@@ -6712,9 +6765,9 @@
|
||||
}
|
||||
},
|
||||
"parity_summary": {
|
||||
"python_indicator_count": 207,
|
||||
"wasm_export_count": 221,
|
||||
"common_python_wasm_count": 91,
|
||||
"python_indicator_count": 210,
|
||||
"wasm_export_count": 222,
|
||||
"common_python_wasm_count": 92,
|
||||
"common_python_wasm": [
|
||||
"ad",
|
||||
"adosc",
|
||||
@@ -6743,6 +6796,7 @@
|
||||
"dema",
|
||||
"detect_breaks_cusum",
|
||||
"donchian",
|
||||
"dtw_distance",
|
||||
"dx",
|
||||
"ema",
|
||||
"ht_dcperiod",
|
||||
@@ -6817,6 +6871,7 @@
|
||||
"asin",
|
||||
"atan",
|
||||
"batch_apply",
|
||||
"batch_dtw",
|
||||
"beta",
|
||||
"cdl2crows",
|
||||
"cdl3blackcrows",
|
||||
@@ -6886,6 +6941,7 @@
|
||||
"cosh",
|
||||
"div",
|
||||
"drawdown",
|
||||
"dtw",
|
||||
"exp",
|
||||
"feature_matrix",
|
||||
"floor",
|
||||
|
||||
+1
-1
@@ -1,7 +1,7 @@
|
||||
Release Notes
|
||||
=============
|
||||
|
||||
These docs track package version ``1.1.3``.
|
||||
These docs track package version ``1.1.4``.
|
||||
|
||||
1.1.0-audit (2026-03-28)
|
||||
------------------------
|
||||
|
||||
@@ -180,7 +180,7 @@ For source builds, packaging details, and platform notes, see
|
||||
Release status
|
||||
--------------
|
||||
|
||||
These docs track package version ``1.1.3``.
|
||||
These docs track package version ``1.1.4``.
|
||||
|
||||
- Release notes by version: :doc:`changelog`
|
||||
- Canonical project changelog: `CHANGELOG.md <https://github.com/pratikbhadane24/ferro-ta/blob/main/CHANGELOG.md>`_
|
||||
|
||||
+1
-1
@@ -4,7 +4,7 @@ build-backend = "maturin"
|
||||
|
||||
[project]
|
||||
name = "ferro-ta"
|
||||
version = "1.1.3"
|
||||
version = "1.1.4"
|
||||
description = "Rust-powered Python technical analysis library with a TA-Lib-compatible API"
|
||||
readme = "README.md"
|
||||
license = { text = "MIT" }
|
||||
|
||||
@@ -12,19 +12,33 @@ LINEARREG_ANGLE — Linear Regression Angle (degrees)
|
||||
TSF — Time Series Forecast
|
||||
BETA — Beta
|
||||
CORREL — Pearson's Correlation Coefficient (r)
|
||||
DTW — Dynamic Time Warping (distance + warping path)
|
||||
DTW_DISTANCE — Dynamic Time Warping distance only (faster)
|
||||
BATCH_DTW — Batch DTW: N series vs 1 reference, in parallel
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Optional
|
||||
|
||||
import numpy as np
|
||||
from numpy.typing import ArrayLike
|
||||
|
||||
from ferro_ta._ferro_ta import (
|
||||
batch_dtw as _batch_dtw,
|
||||
)
|
||||
from ferro_ta._ferro_ta import (
|
||||
beta as _beta,
|
||||
)
|
||||
from ferro_ta._ferro_ta import (
|
||||
correl as _correl,
|
||||
)
|
||||
from ferro_ta._ferro_ta import (
|
||||
dtw as _dtw,
|
||||
)
|
||||
from ferro_ta._ferro_ta import (
|
||||
dtw_distance as _dtw_distance,
|
||||
)
|
||||
from ferro_ta._ferro_ta import (
|
||||
linearreg as _linearreg,
|
||||
)
|
||||
@@ -247,6 +261,98 @@ def CORREL(real0: ArrayLike, real1: ArrayLike, timeperiod: int = 30) -> np.ndarr
|
||||
_normalize_rust_error(e)
|
||||
|
||||
|
||||
def DTW(
|
||||
series1: ArrayLike,
|
||||
series2: ArrayLike,
|
||||
window: Optional[int] = None,
|
||||
) -> tuple[float, np.ndarray]:
|
||||
"""Dynamic Time Warping — distance and optimal warping path.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
series1 : array-like
|
||||
First time series.
|
||||
series2 : array-like
|
||||
Second time series (may differ in length from series1).
|
||||
window : int, optional
|
||||
Sakoe-Chiba band width. ``None`` (default) = unconstrained.
|
||||
|
||||
Returns
|
||||
-------
|
||||
distance : float
|
||||
DTW distance (accumulated Euclidean cost along the optimal path).
|
||||
path : numpy.ndarray, shape (N, 2)
|
||||
Warping path as ``(i, j)`` index pairs from ``(0, 0)`` to
|
||||
``(len(series1)-1, len(series2)-1)``.
|
||||
"""
|
||||
try:
|
||||
return _dtw(_to_f64(series1), _to_f64(series2), window)
|
||||
except ValueError as e:
|
||||
_normalize_rust_error(e)
|
||||
|
||||
|
||||
def DTW_DISTANCE(
|
||||
series1: ArrayLike,
|
||||
series2: ArrayLike,
|
||||
window: Optional[int] = None,
|
||||
) -> float:
|
||||
"""Dynamic Time Warping distance only (faster — no path reconstruction).
|
||||
|
||||
Parameters
|
||||
----------
|
||||
series1 : array-like
|
||||
First time series.
|
||||
series2 : array-like
|
||||
Second time series (may differ in length from series1).
|
||||
window : int, optional
|
||||
Sakoe-Chiba band width. ``None`` (default) = unconstrained.
|
||||
|
||||
Returns
|
||||
-------
|
||||
float
|
||||
DTW distance (accumulated Euclidean cost along the optimal path).
|
||||
"""
|
||||
try:
|
||||
return _dtw_distance(_to_f64(series1), _to_f64(series2), window)
|
||||
except ValueError as e:
|
||||
_normalize_rust_error(e)
|
||||
|
||||
|
||||
def BATCH_DTW(
|
||||
matrix: ArrayLike,
|
||||
reference: ArrayLike,
|
||||
window: Optional[int] = None,
|
||||
) -> np.ndarray:
|
||||
"""Batch Dynamic Time Warping — N series vs 1 reference, computed in parallel.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
matrix : array-like, shape (N, L)
|
||||
N time series of length L. Each row is compared against ``reference``.
|
||||
reference : array-like, shape (L,)
|
||||
The reference series.
|
||||
window : int, optional
|
||||
Sakoe-Chiba band width. ``None`` (default) = unconstrained.
|
||||
|
||||
Returns
|
||||
-------
|
||||
numpy.ndarray, shape (N,)
|
||||
DTW distance from each row of ``matrix`` to ``reference``.
|
||||
"""
|
||||
try:
|
||||
mat = np.ascontiguousarray(matrix, dtype=np.float64)
|
||||
if mat.ndim != 2:
|
||||
from ferro_ta.core.exceptions import FerroTAInputError
|
||||
|
||||
raise FerroTAInputError(
|
||||
f"matrix must be a 2-D array, got {mat.ndim}-D.",
|
||||
suggestion="Pass a 2-D NumPy array of shape (N, L).",
|
||||
)
|
||||
return _batch_dtw(mat, _to_f64(reference), window)
|
||||
except ValueError as e:
|
||||
_normalize_rust_error(e)
|
||||
|
||||
|
||||
__all__ = [
|
||||
"STDDEV",
|
||||
"VAR",
|
||||
@@ -257,4 +363,7 @@ __all__ = [
|
||||
"TSF",
|
||||
"BETA",
|
||||
"CORREL",
|
||||
"DTW",
|
||||
"DTW_DISTANCE",
|
||||
"BATCH_DTW",
|
||||
]
|
||||
|
||||
@@ -0,0 +1,98 @@
|
||||
use ndarray::Array2;
|
||||
use numpy::{IntoPyArray, PyArray1, PyArray2, PyReadonlyArray1, PyReadonlyArray2};
|
||||
use pyo3::exceptions::PyValueError;
|
||||
use pyo3::prelude::*;
|
||||
use rayon::prelude::*;
|
||||
|
||||
/// Dynamic Time Warping — distance and optimal warping path between two 1-D series.
|
||||
///
|
||||
/// Returns a tuple `(distance, path)` where `path` is a NumPy array of shape
|
||||
/// `(N, 2)` containing `(i, j)` index pairs from `(0, 0)` to `(n-1, m-1)`.
|
||||
///
|
||||
/// Local cost: `|series1[i] - series2[j]|` (Euclidean, matches `dtaidistance`).
|
||||
#[pyfunction]
|
||||
#[pyo3(signature = (series1, series2, window = None))]
|
||||
pub fn dtw<'py>(
|
||||
py: Python<'py>,
|
||||
series1: PyReadonlyArray1<'py, f64>,
|
||||
series2: PyReadonlyArray1<'py, f64>,
|
||||
window: Option<usize>,
|
||||
) -> PyResult<(f64, Bound<'py, PyArray2<usize>>)> {
|
||||
let s1 = series1.as_slice()?;
|
||||
let s2 = series2.as_slice()?;
|
||||
if s1.is_empty() || s2.is_empty() {
|
||||
return Err(PyValueError::new_err(
|
||||
"series1 and series2 must not be empty",
|
||||
));
|
||||
}
|
||||
let (dist, path) = ferro_ta_core::statistic::dtw_path(s1, s2, window);
|
||||
let n = path.len();
|
||||
let flat: Vec<usize> = path.iter().flat_map(|&(i, j)| [i, j]).collect();
|
||||
let arr =
|
||||
Array2::from_shape_vec((n, 2), flat).map_err(|e| PyValueError::new_err(e.to_string()))?;
|
||||
Ok((dist, arr.into_pyarray(py)))
|
||||
}
|
||||
|
||||
/// Dynamic Time Warping — distance only (faster, no path reconstruction).
|
||||
///
|
||||
/// Returns the accumulated Euclidean cost along the optimal warping path.
|
||||
/// Use this when you only need the distance, not the alignment path.
|
||||
#[pyfunction]
|
||||
#[pyo3(signature = (series1, series2, window = None))]
|
||||
pub fn dtw_distance<'py>(
|
||||
_py: Python<'py>,
|
||||
series1: PyReadonlyArray1<'py, f64>,
|
||||
series2: PyReadonlyArray1<'py, f64>,
|
||||
window: Option<usize>,
|
||||
) -> PyResult<f64> {
|
||||
let s1 = series1.as_slice()?;
|
||||
let s2 = series2.as_slice()?;
|
||||
if s1.is_empty() || s2.is_empty() {
|
||||
return Err(PyValueError::new_err(
|
||||
"series1 and series2 must not be empty",
|
||||
));
|
||||
}
|
||||
Ok(ferro_ta_core::statistic::dtw_distance(s1, s2, window))
|
||||
}
|
||||
|
||||
/// Batch Dynamic Time Warping — compute DTW distance from each row of a 2-D matrix
|
||||
/// to a single reference series, in parallel.
|
||||
///
|
||||
/// Parameters
|
||||
/// ----------
|
||||
/// matrix : np.ndarray, shape (N, L)
|
||||
/// N time series of length L. Each row is compared against `reference`.
|
||||
/// reference : np.ndarray, shape (L,)
|
||||
/// The reference series.
|
||||
/// window : int, optional
|
||||
/// Sakoe-Chiba band width. `None` = unconstrained.
|
||||
///
|
||||
/// Returns
|
||||
/// -------
|
||||
/// np.ndarray, shape (N,)
|
||||
/// DTW distances, one per row.
|
||||
#[pyfunction]
|
||||
#[pyo3(signature = (matrix, reference, window = None))]
|
||||
pub fn batch_dtw<'py>(
|
||||
py: Python<'py>,
|
||||
matrix: PyReadonlyArray2<'py, f64>,
|
||||
reference: PyReadonlyArray1<'py, f64>,
|
||||
window: Option<usize>,
|
||||
) -> PyResult<Bound<'py, PyArray1<f64>>> {
|
||||
let mat = matrix.as_array();
|
||||
let ref_slice = reference.as_slice()?;
|
||||
|
||||
if ref_slice.is_empty() {
|
||||
return Err(PyValueError::new_err("reference must not be empty"));
|
||||
}
|
||||
|
||||
let (n_rows, _) = mat.dim();
|
||||
let rows: Vec<Vec<f64>> = (0..n_rows).map(|i| mat.row(i).to_vec()).collect();
|
||||
|
||||
let result: Vec<f64> = rows
|
||||
.par_iter()
|
||||
.map(|series| ferro_ta_core::statistic::dtw_distance(series, ref_slice, window))
|
||||
.collect();
|
||||
|
||||
Ok(result.into_pyarray(py))
|
||||
}
|
||||
@@ -4,6 +4,7 @@
|
||||
mod beta;
|
||||
pub(crate) mod common;
|
||||
mod correl;
|
||||
mod dtw;
|
||||
mod linearreg;
|
||||
mod stddev;
|
||||
mod var;
|
||||
@@ -23,5 +24,8 @@ pub fn register(m: &Bound<'_, PyModule>) -> PyResult<()> {
|
||||
m.add_function(pyo3::wrap_pyfunction!(self::linearreg::tsf, m)?)?;
|
||||
m.add_function(pyo3::wrap_pyfunction!(self::beta::beta, m)?)?;
|
||||
m.add_function(pyo3::wrap_pyfunction!(self::correl::correl, m)?)?;
|
||||
m.add_function(pyo3::wrap_pyfunction!(self::dtw::dtw, m)?)?;
|
||||
m.add_function(pyo3::wrap_pyfunction!(self::dtw::dtw_distance, m)?)?;
|
||||
m.add_function(pyo3::wrap_pyfunction!(self::dtw::batch_dtw, m)?)?;
|
||||
Ok(())
|
||||
}
|
||||
|
||||
@@ -1,10 +1,14 @@
|
||||
"""Unit tests for ferro_ta.indicators.statistic"""
|
||||
|
||||
import numpy as np
|
||||
import pytest
|
||||
|
||||
from ferro_ta.indicators.statistic import (
|
||||
BATCH_DTW,
|
||||
BETA,
|
||||
CORREL,
|
||||
DTW,
|
||||
DTW_DISTANCE,
|
||||
LINEARREG,
|
||||
LINEARREG_ANGLE,
|
||||
LINEARREG_INTERCEPT,
|
||||
@@ -308,3 +312,177 @@ class TestTSF:
|
||||
expected = _naive_linearreg(_A, timeperiod=14, x_value=14.0)
|
||||
result = TSF(_A, timeperiod=14)
|
||||
np.testing.assert_allclose(result, expected, equal_nan=True)
|
||||
|
||||
|
||||
# ---------------------------------------------------------------------------
|
||||
# DTW — Dynamic Time Warping
|
||||
# ---------------------------------------------------------------------------
|
||||
|
||||
dtai = pytest.importorskip("dtaidistance", reason="dtaidistance not installed")
|
||||
|
||||
_DTW_RNG = np.random.default_rng(42)
|
||||
|
||||
|
||||
class TestDTW:
|
||||
# --- Validation against dtaidistance (SOTA reference) ---
|
||||
|
||||
def test_distance_matches_dtaidistance_random(self):
|
||||
"""Core correctness: our distance == dtaidistance on 20 random pairs."""
|
||||
for _ in range(20):
|
||||
n = int(_DTW_RNG.integers(5, 50))
|
||||
a = _DTW_RNG.random(n)
|
||||
b = _DTW_RNG.random(n)
|
||||
expected = dtai.dtw.distance(a, b)
|
||||
actual = DTW_DISTANCE(a, b)
|
||||
np.testing.assert_allclose(
|
||||
actual, expected, rtol=1e-9, err_msg=f"Mismatch on series length {n}"
|
||||
)
|
||||
|
||||
def test_distance_matches_dtaidistance_unequal_length(self):
|
||||
"""Handles unequal-length series correctly."""
|
||||
for _ in range(10):
|
||||
a = _DTW_RNG.random(int(_DTW_RNG.integers(5, 30)))
|
||||
b = _DTW_RNG.random(int(_DTW_RNG.integers(5, 30)))
|
||||
expected = dtai.dtw.distance(a, b)
|
||||
actual = DTW_DISTANCE(a, b)
|
||||
np.testing.assert_allclose(actual, expected, rtol=1e-9)
|
||||
|
||||
def test_path_distance_matches_dtaidistance(self):
|
||||
"""DTW() path variant: returned distance matches dtaidistance."""
|
||||
a = _DTW_RNG.random(20)
|
||||
b = _DTW_RNG.random(25)
|
||||
expected = dtai.dtw.distance(a, b)
|
||||
dist, _ = DTW(a, b)
|
||||
np.testing.assert_allclose(dist, expected, rtol=1e-9)
|
||||
|
||||
def test_path_matches_dtaidistance_warping_path(self):
|
||||
"""Warping path matches dtaidistance.dtw.warping_path() on same-length series."""
|
||||
for _ in range(10):
|
||||
n = int(_DTW_RNG.integers(5, 20))
|
||||
a = _DTW_RNG.random(n)
|
||||
b = _DTW_RNG.random(n)
|
||||
expected_path = dtai.dtw.warping_path(a, b)
|
||||
_, actual_path = DTW(a, b)
|
||||
actual_pairs = [tuple(int(x) for x in row) for row in actual_path]
|
||||
assert actual_pairs == expected_path, (
|
||||
f"Path mismatch for n={n}:\n ours={actual_pairs}\n dtai={expected_path}"
|
||||
)
|
||||
|
||||
def test_window_constrained_matches_dtaidistance(self):
|
||||
"""Sakoe-Chiba window matches dtaidistance window parameter."""
|
||||
a = _DTW_RNG.random(30)
|
||||
b = _DTW_RNG.random(30)
|
||||
for w in [3, 8, 15]:
|
||||
expected = dtai.dtw.distance(a, b, window=w)
|
||||
actual = DTW_DISTANCE(a, b, window=w)
|
||||
np.testing.assert_allclose(
|
||||
actual, expected, rtol=1e-9, err_msg=f"Mismatch at window={w}"
|
||||
)
|
||||
|
||||
def test_batch_matches_dtaidistance(self):
|
||||
"""BATCH_DTW matches calling dtaidistance per-row."""
|
||||
ref = _DTW_RNG.random(20)
|
||||
matrix = _DTW_RNG.random((8, 20))
|
||||
batch_result = BATCH_DTW(matrix, ref)
|
||||
for i in range(8):
|
||||
expected = dtai.dtw.distance(matrix[i], ref)
|
||||
np.testing.assert_allclose(
|
||||
batch_result[i],
|
||||
expected,
|
||||
rtol=1e-9,
|
||||
err_msg=f"Batch mismatch at row {i}",
|
||||
)
|
||||
|
||||
# --- Mathematical properties ---
|
||||
|
||||
def test_identical_distance_is_zero(self):
|
||||
a = np.array([1.0, 2.0, 3.0, 4.0, 5.0])
|
||||
dist, _ = DTW(a, a)
|
||||
assert dist == pytest.approx(0.0, abs=1e-10)
|
||||
|
||||
def test_symmetry(self):
|
||||
a, b = _DTW_RNG.random(20), _DTW_RNG.random(20)
|
||||
assert DTW_DISTANCE(a, b) == pytest.approx(DTW_DISTANCE(b, a), rel=1e-10)
|
||||
|
||||
def test_triangle_inequality(self):
|
||||
a, b, c = _DTW_RNG.random(15), _DTW_RNG.random(15), _DTW_RNG.random(15)
|
||||
assert DTW_DISTANCE(a, c) <= DTW_DISTANCE(a, b) + DTW_DISTANCE(b, c) + 1e-9
|
||||
|
||||
# --- Known hardcoded values ---
|
||||
|
||||
def test_known_shifted_series(self):
|
||||
# [0,1,2] vs [1,2,3]: optimal path (0,0)→(1,0)→(2,1)→(2,2)
|
||||
# Squared costs: 1+0+0+1=2, sqrt(2). Verified against dtaidistance.
|
||||
a = np.array([0.0, 1.0, 2.0])
|
||||
b = np.array([1.0, 2.0, 3.0])
|
||||
np.testing.assert_allclose(DTW_DISTANCE(a, b), np.sqrt(2.0), rtol=1e-9)
|
||||
|
||||
def test_known_single_element(self):
|
||||
# sqrt((3-7)^2) = sqrt(16) = 4.0
|
||||
np.testing.assert_allclose(
|
||||
DTW_DISTANCE(np.array([3.0]), np.array([7.0])), 4.0, rtol=1e-9
|
||||
)
|
||||
|
||||
def test_known_constant_series(self):
|
||||
assert DTW_DISTANCE(np.full(10, 5.0), np.full(10, 5.0)) == pytest.approx(
|
||||
0.0, abs=1e-12
|
||||
)
|
||||
|
||||
# --- Path structural guarantees ---
|
||||
|
||||
def test_path_starts_at_origin(self):
|
||||
_, path = DTW(_DTW_RNG.random(10), _DTW_RNG.random(10))
|
||||
assert tuple(int(x) for x in path[0]) == (0, 0)
|
||||
|
||||
def test_path_ends_at_corner(self):
|
||||
_, path = DTW(_DTW_RNG.random(7), _DTW_RNG.random(9))
|
||||
assert tuple(int(x) for x in path[-1]) == (6, 8)
|
||||
|
||||
def test_path_is_monotone(self):
|
||||
_, path = DTW(_DTW_RNG.random(20), _DTW_RNG.random(20))
|
||||
for k in range(1, len(path)):
|
||||
assert path[k][0] >= path[k - 1][0]
|
||||
assert path[k][1] >= path[k - 1][1]
|
||||
|
||||
def test_path_steps_unit_size(self):
|
||||
_, path = DTW(_DTW_RNG.random(15), _DTW_RNG.random(12))
|
||||
for k in range(1, len(path)):
|
||||
di = int(path[k][0]) - int(path[k - 1][0])
|
||||
dj = int(path[k][1]) - int(path[k - 1][1])
|
||||
assert di in (0, 1) and dj in (0, 1)
|
||||
assert not (di == 0 and dj == 0)
|
||||
|
||||
# --- DTW_DISTANCE == DTW distance ---
|
||||
|
||||
def test_distance_only_matches_full(self):
|
||||
a, b = _DTW_RNG.random(25), _DTW_RNG.random(25)
|
||||
d_full, _ = DTW(a, b)
|
||||
np.testing.assert_allclose(DTW_DISTANCE(a, b), d_full, rtol=1e-10)
|
||||
|
||||
# --- Batch ---
|
||||
|
||||
def test_batch_single_row(self):
|
||||
ref = np.array([1.0, 2.0, 3.0])
|
||||
result = BATCH_DTW(np.array([[1.0, 2.0, 3.0]]), ref)
|
||||
assert result[0] == pytest.approx(0.0, abs=1e-10)
|
||||
|
||||
def test_batch_matches_single_calls(self):
|
||||
ref = _DTW_RNG.random(20)
|
||||
matrix = _DTW_RNG.random((8, 20))
|
||||
batch = BATCH_DTW(matrix, ref)
|
||||
for i in range(8):
|
||||
np.testing.assert_allclose(
|
||||
batch[i], DTW_DISTANCE(matrix[i], ref), rtol=1e-10
|
||||
)
|
||||
|
||||
# --- Edge cases ---
|
||||
|
||||
def test_empty_series_raises(self):
|
||||
with pytest.raises((ValueError, Exception)):
|
||||
DTW(np.array([]), np.array([1.0, 2.0]))
|
||||
|
||||
def test_window_constrained_ge_unconstrained(self):
|
||||
a, b = _DTW_RNG.random(20), _DTW_RNG.random(20)
|
||||
d_full = DTW_DISTANCE(a, b)
|
||||
d_narrow = DTW_DISTANCE(a, b, window=2)
|
||||
assert d_narrow >= d_full - 1e-9
|
||||
|
||||
@@ -950,7 +950,7 @@ wheels = [
|
||||
|
||||
[[package]]
|
||||
name = "ferro-ta"
|
||||
version = "1.1.3"
|
||||
version = "1.1.4"
|
||||
source = { editable = "." }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
|
||||
Generated
+2
-2
@@ -49,11 +49,11 @@ checksum = "9330f8b2ff13f34540b44e946ef35111825727b38d33286ef986142615121801"
|
||||
|
||||
[[package]]
|
||||
name = "ferro_ta_core"
|
||||
version = "1.1.3"
|
||||
version = "1.1.4"
|
||||
|
||||
[[package]]
|
||||
name = "ferro_ta_wasm"
|
||||
version = "1.1.3"
|
||||
version = "1.1.4"
|
||||
dependencies = [
|
||||
"ferro_ta_core",
|
||||
"js-sys",
|
||||
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
[package]
|
||||
name = "ferro_ta_wasm"
|
||||
version = "1.1.3"
|
||||
version = "1.1.4"
|
||||
edition = "2021"
|
||||
description = "WebAssembly bindings for ferro-ta technical analysis indicators"
|
||||
license = "MIT"
|
||||
|
||||
+1
-1
@@ -1,6 +1,6 @@
|
||||
{
|
||||
"name": "ferro-ta-wasm",
|
||||
"version": "1.1.3",
|
||||
"version": "1.1.4",
|
||||
"description": "WebAssembly bindings for ferro-ta technical analysis indicators",
|
||||
"main": "node/ferro_ta_wasm.js",
|
||||
"module": "web/ferro_ta_wasm.js",
|
||||
|
||||
@@ -1653,6 +1653,18 @@ pub fn correl(real0: &Float64Array, real1: &Float64Array, timeperiod: usize) ->
|
||||
from_vec(ferro_ta_core::statistic::correl(&to_vec(real0), &to_vec(real1), timeperiod))
|
||||
}
|
||||
|
||||
/// Dynamic Time Warping distance between two series.
|
||||
///
|
||||
/// Returns the accumulated Euclidean cost along the optimal warping path.
|
||||
/// Pass `window` as `0` for unconstrained (no Sakoe-Chiba band).
|
||||
#[wasm_bindgen]
|
||||
pub fn dtw_distance(series1: &Float64Array, series2: &Float64Array, window: usize) -> f64 {
|
||||
let s1 = to_vec(series1);
|
||||
let s2 = to_vec(series2);
|
||||
let w = if window == 0 { None } else { Some(window) };
|
||||
ferro_ta_core::statistic::dtw_distance(&s1, &s2, w)
|
||||
}
|
||||
|
||||
// ===========================================================================
|
||||
// Streaming / Stateful API
|
||||
// ===========================================================================
|
||||
|
||||
Reference in New Issue
Block a user