Fixing issues with alpha model and new TVL size risk model (#1051)

- Add safety asserts
- Allow empty normalised weights

tradeexecutor/strategy/alpha_model.py

@@ -485,6 +485,30 @@ def get_debug_print(self) -> str:
             print(f"   Signal #{idx} {signal}", file=buf)
         return buf.getvalue()
 
+    def has_any_signal(self) -> bool:
+        """For this cycle, should we try to do any trades.
+
+        Any of the signals have non-zero value
+
+        :return:
+            True if alpha model should attempt to do some trades on this timestamp/cycle.
+
+            Trades could be still cancelled (zeroed out) by a risk model.
+        """
+        return any([s for s in self.signals.values() if s.signal != 0])
+
+    def has_any_position(self) -> bool:
+        """For this cycle, are we going to do any trades.
+
+        Some signals have :py:attr:`TradingSignal.position_target` set after the risk adjustments.
+
+        :return:
+            True if alpha model should attempt to do some trades on this timestamp/cycle.
+
+            Trades could be still cancelled (zeroed out) by a risk model.
+        """
+        return any([s for s in self.signals.values() if s.position_target != 0])
+
     def set_signal(
             self,
             pair: TradingPairIdentifier,
@@ -677,6 +701,8 @@ def _normalise_weights_size_risk(
             # if all positions are size-risked down
             s = self.signals[pair_id]
 
+            assert s.old_weight is not None, f"TradingSignal.old_weight is not available: {s} - remember to call AlphaModel.update_old_weights()"
+
             assert s.raw_weight >= 0, "_normalise_weights_size_risk(): short or leverage not implemented"
 
             concentration_capped_normal_weight = min(normal_weight, max_weight)
@@ -687,6 +713,14 @@ def _normalise_weights_size_risk(
                 asked_position_size
             )
 
+            logger.info(
+                "Position size risk, pair: %s, asked: %s, accepted: %s, diagnostics: %s",
+                s.pair,
+                size_risk.asked_size,
+                size_risk.accepted_size,
+                size_risk.diagnostics_data,
+            )
+
             s.position_size_risk = size_risk
             s.position_target = size_risk.accepted_size
             total_accetable_investments += size_risk.accepted_size
@@ -765,6 +799,15 @@ def normalise_weights(
                 investable_equity=investable_equity,
             )
 
+        # Risk model zeroed out everything so something is likely wrong
+        if self.has_any_signal() and not self.has_any_position():
+            logger.warning(
+                "normalise_weights() at %s had signal, but refuses to have any position target, all positions zeroed out by risk model",
+                self.timestamp,
+            )
+            for s in self.signals.values():
+                logger.warning("Signal %s", s)
+
     def assign_weights(self, method=weight_by_1_slash_n):
         """Convert raw signals to their portfolio weight counterparts.
 

tradeexecutor/strategy/runner.py

@@ -1075,11 +1075,13 @@ def post_process_trade_decision(
         If any of the trades is detected to have too much price impact.
     """
 
-    logger.info(
-        "post_process_trade_decision(): Post-processing %d trades, max_price_impact: %s",
-        len(trades),
-        max_price_impact,
-    )
+    if not execution_context.mode.is_backtesting():
+        # Too noisy for backtesting
+        logger.info(
+            "post_process_trade_decision(): Post-processing %d trades, max_price_impact: %s",
+            len(trades),
+            max_price_impact,
+        )
 
     # TODO: Write a full logic here, only supports closing shorts now,
     # assuming everything lending is short

tradeexecutor/strategy/trading_strategy_universe.py

@@ -1951,7 +1951,6 @@ def load_partial_data(
     name: str | None = None,
     candle_progress_bar_desc: str | None = None,
     lending_candle_progress_bar_desc: str | None = None,
-
 ) -> Dataset:
     """Load pair data for given trading pairs.
 
@@ -2009,15 +2008,6 @@ def create_trading_universe(
     :param time_bucket:
         The candle time frame.
 
-    :param chain_id:
-        Which blockchain hosts our exchange
-
-    :param exchange_slug:
-        Which exchange hosts our trading pairs
-
-    :param exchange_slug:
-        Which exchange hosts our trading pairs
-
     :param pairs:
 
         List of trading pair tickers.
@@ -2027,11 +2017,14 @@ def create_trading_universe(
         - Human-readable descriptions, see :py:attr:`tradingstrategy.pair.HumanReadableTradingPairDescription`.
         - Direct :py:class:`pandas.DataFrame` of pairs.
 
-    :param lending_reserves:
+    :param lending_resserves:
         Lending reserves for which you want to download the data.
 
         Either list of lending pool descriptions or preloaded lending universe.
 
+    :param lending_candle_types:
+        What lending data columns to load
+
     :param liquidity:
         Set true to load liquidity data as well
 
@@ -2040,9 +2033,12 @@ def create_trading_universe(
 
         If not given use `time_bucket`.
 
-    :param lending_reverses:
+    :param lending_reserves:
         Set true to load lending reserve data as well
 
+    :lending_candle_types:
+        What lending data columns to load
+
     :param stop_loss_time_bucket:
         If set load stop loss trigger
         data using this candle granularity.

tradeexecutor/strategy/weighting.py

@@ -110,6 +110,10 @@ def normalise_weights(weights: Dict[PairInternalId, Weight]) -> Dict[PairInterna
 
     total = sum(weights.values())
     normalised_weights = {}
+    if total == 0:
+        # Avoid division by zero
+        return normalised_weights
+
     for key, value in weights.items():
         normalised_weights[key] = value / total