✨ Add metrics timeseries

This timeseries will have both `std_dev` and `returns` observations

:bug: Validate `returns` and `std_dev` timeseries

:bug:  Sort the index of price series

This fixes an issue where the std_dev ts would be incorrect,
since the rolling window calcs are applied on a decending ordered ts.

This means that the last seven days would not have std_dev observations,
but the first seven would.

:rewind: Reintroduce asset property for frontend

backend/app/endpoints.py

@@ -1,5 +1,6 @@
 import datetime
 from dataclasses import asdict, dataclass
+from json import loads
 from typing import Iterable, Literal, TypeVar, Union
 
 import dateutil
@@ -21,6 +22,7 @@
 class PortfolioAsset:
     allocation: float
     volatility: float
+    metrics: dict[str, float]
     risk_contribution: float
 
 
@@ -54,13 +56,20 @@ def from_treasury_with_assets(
 
         assets = {
             a.token_symbol: PortfolioAsset(
-                allocation=a.balance_usd / treasury.usd_total,
-                volatility=None
-                if histprices[a.token_symbol]["std_dev"].mean() is NaN
-                else histprices[a.token_symbol]["std_dev"].mean(),
+                allocation=a.balance / treasury.usd_total,
+                metrics={}
+                if histprices[a.token_symbol][["std_dev", "returns"]].empty
+                else loads(
+                    histprices[a.token_symbol][["std_dev", "returns"]].to_json(
+                        orient="index", date_format="iso"
+                    )
+                ),
                 risk_contribution=a.risk_contribution
                 if hasattr(a, "risk_contribution")
                 else None,
+                volatility=None
+                if histprices[a.token_symbol]["std_dev"].mean() is NaN
+                else histprices[a.token_symbol]["std_dev"].mean(),
             )
             for a in treasury.assets
         }

backend/app/libs/price_stats.py

@@ -18,7 +18,7 @@ def make_returns_df(hist_values: pd.Series, column_name: str) -> pd.DataFrame:
     dataframe.returns.replace(-np.inf, 0, inplace=True)
     dataframe.returns.iloc[1:].replace(
         np.nan, 0, inplace=True
-    )  # Keep first nan (probly useless).
+    )  # Keep first nan (probably useless).
     dataframe["std_dev"] = dataframe.returns.rolling(ROLLING_WINDOW_DAYS).std(ddof=0)
     return dataframe
 

backend/app/libs/series.py

@@ -11,4 +11,4 @@ def make_hist_price_series(
         (price.value for price in prices),
         index=pd.Index((price.timestamp for price in prices), name="timestamp"),
         name=f"{token_symbol} historical price",
-    )
+    ).sort_index()