Merge pull request #560 from coding-kitties/dev

Release: pluggable slippage models, index enhancements & evaluation focus

Author: MDUYN

README.md

@@ -343,6 +343,60 @@ backtests = [store.open(row["bundle_path"]) for row in top]
 BacktestReport(backtests=backtests).save("top20.html")
 ```
 
+#### Weighted multi-metric ranking with `BacktestEvaluationFocus`
+
+Instead of sorting by a single column, use a **focus preset** to score every bundle across multiple metrics at once — profit, risk, consistency, win rate — weighted by what matters most to your workflow:
+
+```python
+from investing_algorithm_framework import (
+    BacktestReport, BacktestEvaluationFocus,
+)
+from investing_algorithm_framework.cli.index_command import (
+    build_index, rank_index,
+)
+from investing_algorithm_framework.services.backtest_store import (
+    LocalDirStore,
+)
+
+# 1. Build (or refresh) the Tier-1 SQLite index.
+build_index("./my-backtests/")
+
+# 2. Rank with a built-in focus preset (BALANCED, PROFIT, FREQUENCY, RISK_ADJUSTED).
+top = rank_index(
+    "./my-backtests/",
+    focus=BacktestEvaluationFocus.RISK_ADJUSTED,
+    where="summary_number_of_trades > 50",
+    limit=25,
+)
+
+# 3. Or supply fully custom weights — positive favours higher, negative penalises.
+top = rank_index(
+    "./my-backtests/",
+    weights={
+        "sharpe_ratio": 3.0,
+        "sortino_ratio": 2.5,
+        "max_drawdown": -3.0,
+        "win_rate": 2.0,
+        "consistency_score": 1.5,
+    },
+    limit=25,
+)
+
+# 4. Materialise only the winners and render a focused dashboard.
+store = LocalDirStore("./my-backtests/")
+winners = [store.open(row["bundle_path"]) for row in top]
+BacktestReport(backtests=winners).save("top25_risk_adjusted.html")
+```
+
+**Built-in focus presets:**
+
+| Preset | Prioritises |
+|--------|------------|
+| `BALANCED` | Equal mix of profit, risk-adjusted returns, drawdown penalties, and consistency |
+| `PROFIT` | Absolute and relative gains (CAGR, net gain, win rate, profit factor) |
+| `FREQUENCY` | High trade count, short durations, and per-trade efficiency |
+| `RISK_ADJUSTED` | Sharpe, Sortino, Calmar with strong drawdown and volatility penalties |
+
 Or from the shell:
 
 ```bash

docusaurus/docs/Advanced Concepts/blotter.md

@@ -75,6 +75,37 @@ app.set_blotter(SimulationBlotter(
 ))
 ```
 
+### VolumeShareSlippage
+
+Volume-aware model with quadratic price impact. Limits fills to a fraction of bar volume, producing partial fills for large orders relative to liquidity.
+
+```python
+from investing_algorithm_framework import SimulationBlotter, VolumeShareSlippage
+
+app.set_blotter(SimulationBlotter(
+    slippage_model=VolumeShareSlippage(
+        volume_limit=0.025,    # max 2.5% of bar volume
+        price_impact=0.1,      # quadratic impact coefficient
+    )
+))
+```
+
+### FixedBasisPointsSlippage
+
+Slippage expressed in basis points (1 bp = 0.01 % of price).
+
+```python
+from investing_algorithm_framework import SimulationBlotter, FixedBasisPointsSlippage
+
+app.set_blotter(SimulationBlotter(
+    slippage_model=FixedBasisPointsSlippage(basis_points=5)  # 5 bps = 0.05%
+))
+```
+
+:::tip TradingCost Integration
+Slippage models can also be attached directly to a `TradingCost` via the `slippage_model` parameter, without needing a custom blotter. See [TradingCost — Slippage Models](../Risk%20Rules/trading-cost.md#slippage-models) for details.
+:::
+
 ### Custom Slippage Model
 
 Create your own by extending `SlippageModel`:

docusaurus/docs/Risk Rules/overview.md

@@ -66,7 +66,7 @@ class MyStrategy(TradingStrategy):
 | `take_profits` | [`TakeProfitRule`](./take-profit-rule.md) | Bar-end exit when price rises a fixed or trailing percentage from entry / peak. |
 | `scaling_rules` | [`ScalingRule`](./scaling-rule.md) | Pyramid into winners and partially close — `max_entries`, `scale_in_percentage`, `scale_out_percentage`, optional `max_position_percentage` cap. |
 | `cooldowns` | [`CooldownRule`](./cooldown-rule.md) | Side-aware, per-symbol or portfolio-wide signal throttling after fills. |
-| `trading_costs` | [`TradingCost`](./trading-cost.md) | Per-symbol fees and slippage applied during fill simulation. |
+| `trading_costs` | [`TradingCost`](./trading-cost.md) | Per-symbol fees and slippage applied during fill simulation. Supports [pluggable slippage models](./trading-cost.md#slippage-models) (volume-based, fixed spread, basis points). |
 
 ## Where Rules Are Enforced
 

docusaurus/docs/Risk Rules/trading-cost.md

@@ -18,15 +18,17 @@ TradingCost(
     fee_percentage: float = 0.0,
     slippage_percentage: float = 0.0,
     fee_fixed: float = 0.0,
+    slippage_model: SlippageModel | None = None,
 )
 ```
 
 | Parameter | Type | Default | Description |
 |---|---|---|---|
 | `symbol` | `str \| None` | `None` | Target symbol (e.g. `"BTC"`). `None` means "market default" when used at the portfolio level. Symbol matching is case-insensitive. |
 | `fee_percentage` | `float` | `0.0` | Variable fee in **percent of trade value** (e.g. `0.1` = 0.1 %). |
-| `slippage_percentage` | `float` | `0.0` | Slippage in **percent of price**. Buys fill higher, sells fill lower. |
+| `slippage_percentage` | `float` | `0.0` | Slippage in **percent of price**. Buys fill higher, sells fill lower. Ignored when `slippage_model` is set. |
 | `fee_fixed` | `float` | `0.0` | Flat fee per trade in the trading currency, added on top of `fee_percentage`. |
+| `slippage_model` | `SlippageModel \| None` | `None` | Pluggable slippage model. When set, **overrides** `slippage_percentage`. See [Slippage Models](#slippage-models) below. |
 
 ## How Costs Are Applied
 
@@ -39,6 +41,8 @@ sell_fill_price = price * (1 - slippage_percentage / 100)
 fee = trade_value * fee_percentage / 100 + fee_fixed
 ```
 
+When a `slippage_model` is set, the model's `calculate_slippage()` method replaces the percentage formula above. The fee calculation stays the same.
+
 `trade_value` is computed at the slippage-adjusted price, so fees compound on top of slippage — matching how real exchanges quote post-trade cost.
 
 ## Resolution Order
@@ -118,8 +122,137 @@ PortfolioConfiguration(
 - **`StopLossRule` / `TakeProfitRule`** — slippage is applied to the exit fill (`sell` direction), so reported PnL already reflects realistic exits.
 - **`ScalingRule`** — every scale-in and scale-out is a separate fill and pays fees independently.
 
+## Slippage Models
+
+The `slippage_model` parameter lets you plug in sophisticated slippage behavior that goes beyond a flat percentage. When set, it **overrides** the `slippage_percentage` field.
+
+```python
+from investing_algorithm_framework import (
+    TradingCost,
+    VolumeShareSlippage,
+    FixedSlippage,
+    FixedBasisPointsSlippage,
+)
+```
+
+### VolumeShareSlippage
+
+Models slippage as a function of the order's share of bar volume with a **quadratic** price impact. Also enforces a volume limit — at most `volume_limit` fraction of a bar's volume can be filled per bar. Orders exceeding this limit are partially filled and re-evaluated on subsequent bars.
+
+```python
+TradingCost(
+    symbol="BTC",
+    fee_percentage=0.1,
+    slippage_model=VolumeShareSlippage(
+        volume_limit=0.025,    # max 2.5% of bar volume
+        price_impact=0.1,      # price impact coefficient
+    ),
+)
+```
+
+| Parameter | Default | Description |
+|---|---|---|
+| `volume_limit` | `0.025` | Max fraction of bar volume that can fill per bar (0.025 = 2.5 %). |
+| `price_impact` | `0.1` | Coefficient for quadratic impact: `impact = price_impact × (amount / volume)²`. |
+
+**Impact formula:**
+
+```text
+participation = amount / volume
+impact        = price_impact * participation²
+
+buy_fill_price  = price * (1 + impact)
+sell_fill_price = price * (1 - impact)
+```
+
+This is the most realistic built-in model — strategies that trade illiquid assets or large positions relative to volume will see significant market impact, and orders larger than the volume limit will be partially filled.
+
+### FixedSlippage
+
+Adds or subtracts a fixed amount from the order price. Useful for markets with a known, relatively stable spread.
+
+```python
+TradingCost(
+    symbol="ETH",
+    fee_percentage=0.1,
+    slippage_model=FixedSlippage(amount=0.50),  # fixed $0.50 spread
+)
+```
+
+| Parameter | Default | Description |
+|---|---|---|
+| `amount` | `0.01` | Fixed slippage in price units. |
+
+### FixedBasisPointsSlippage
+
+Slippage expressed in basis points (1 bp = 0.01 % of price). Convenient when you want a proportional slippage without thinking in decimals.
+
+```python
+TradingCost(
+    symbol="BTC",
+    fee_percentage=0.1,
+    slippage_model=FixedBasisPointsSlippage(basis_points=5),  # 5 bps = 0.05%
+)
+```
+
+| Parameter | Default | Description |
+|---|---|---|
+| `basis_points` | `5` | Slippage in basis points. |
+
+### Custom Slippage Model
+
+Create your own by extending `SlippageModel`:
+
+```python
+from investing_algorithm_framework import SlippageModel
+
+class MySlippageModel(SlippageModel):
+    def __init__(self, price_impact=0.1, volume_limit=0.025):
+        self.price_impact = price_impact
+        self.volume_limit = volume_limit
+
+    def calculate_slippage(self, price, order_side, amount=None, volume=None):
+        """Return adjusted fill price."""
+        if amount and volume and volume > 0:
+            impact = self.price_impact * (amount / volume) ** 2
+        else:
+            impact = 0.0
+
+        if order_side == "BUY":
+            return price * (1 + impact)
+        return price * (1 - impact)
+
+    def max_fill_amount(self, order_amount, volume=None):
+        """Return maximum fillable amount for this bar."""
+        if volume and volume > 0:
+            return min(order_amount, volume * self.volume_limit)
+        return order_amount
+```
+
+The two methods you can override:
+
+| Method | Required | Description |
+|---|---|---|
+| `calculate_slippage(price, order_side, amount, volume)` | Yes | Return the adjusted fill price after slippage. |
+| `max_fill_amount(order_amount, volume)` | No | Return the maximum fillable amount per bar. Default returns the full `order_amount` (no volume limit). |
+
+### Choosing a Slippage Approach
+
+| Approach | When to use |
+|---|---|
+| `slippage_percentage` | Quick approximation, don't need volume awareness. |
+| `FixedSlippage` | Known fixed spread (e.g. a specific venue). |
+| `FixedBasisPointsSlippage` | Proportional slippage in familiar units (basis points). |
+| `VolumeShareSlippage` | Realistic simulation — large orders impact price, fills are volume-limited. |
+| Custom `SlippageModel` | Any other behavior (e.g. time-of-day effects, asymmetric slippage). |
+
+:::tip Backward Compatibility
+Setting `slippage_model` is fully optional. Existing strategies using `slippage_percentage` continue to work unchanged.
+:::
+
 ## See Also
 
 - [Execution Logic](../Advanced%20Concepts/execution-logic.md)
+- [Blotter](../Advanced%20Concepts/blotter.md)
 - [Orders](../Getting%20Started/orders.md)
 - [Risk Rules Overview](./overview.md)

examples/tutorial/notebooks/03_param_sweep.ipynb

@@ -484,7 +484,14 @@
    "id": "9",
    "metadata": {},
    "source": [
-    "## Quick filter via SQLite index & open report"
+    "## Pruning & Ranking Backtests\n",
+    "\n",
+    "After the parameter sweep completes, we often have dozens (or hundreds) of backtest bundles on disk. Rather than decoding every `.iafbt` file to compare them, we use the **Tier-1 SQLite index** to rank and prune in milliseconds:\n",
+    "\n",
+    "1. **`build_index()`** — scans the results directory and promotes every scalar metric from each bundle into a single SQLite table (one row per backtest). This only touches the lightweight Parquet header, so it scales to 10k+ bundles.\n",
+    "2. **`rank_index()`** — runs a pure-SQL sort/filter on that index. Here we rank by Sharpe ratio and require at least 5 closed trades, returning the top 20 instantly — no full bundle decode needed.\n",
+    "3. **`prune_backtests()`** — moves every bundle *not* in the top-N list to an archive folder (`_pruned/`). With `flatten=True` the archive is a flat directory for easy browsing. Bundles are never deleted, so you can always recover them.\n",
+    "4. **`LocalDirStore.open()`** — materialises only the winning bundles into full `Backtest` objects for the HTML dashboard."
    ]
   },
   {
@@ -494,7 +501,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from investing_algorithm_framework import BacktestReport\n",
+    "from investing_algorithm_framework import BacktestReport, BacktestEvaluationFocus\n",
     "from investing_algorithm_framework.cli.index_command import (\n",
     "    build_index, rank_index, format_table, prune_backtests,\n",
     ")\n",
@@ -510,14 +517,17 @@
     ")\n",
     "print(f\"Index written to: {index_path}\")\n",
     "\n",
-    "# 2. Rank by Sharpe and keep the top 20 — pure SQL, instant.\n",
+    "# 2. Rank using weighted multi-metric scoring with a BALANCED focus.\n",
+    "#    This scores every bundle across profit, risk-adjusted returns,\n",
+    "#    drawdown penalties, and cross-window consistency — all from SQLite.\n",
+    "#    Available presets: BALANCED, PROFIT, FREQUENCY, RISK_ADJUSTED.\n",
     "top = rank_index(\n",
     "    str(backtest_results_dir),\n",
-    "    by=\"sharpe_ratio\",\n",
+    "    focus=BacktestEvaluationFocus.BALANCED,\n",
     "    where=\"summary_number_of_trades_closed > 5\",\n",
     "    limit=20,\n",
     ")\n",
-    "print(f\"\\nTop {len(top)} strategies by Sharpe ratio:\\n\")\n",
+    "print(f\"\\nTop {len(top)} strategies (BALANCED focus):\\n\")\n",
     "print(format_table(top))\n",
     "\n",
     "# 3. Prune: move everything outside the top 20 to an archive folder.\n",
@@ -528,6 +538,7 @@
     "    keep=top,\n",
     "    archive_dir=str(archive_path),\n",
     "    show_progress=True,\n",
+    "    flatten=True,  # flat archive for easy browsing — no nested folders\n",
     ")\n",
     "print(f\"\\nKept {result['kept']} bundles, pruned {result['pruned']} → {result['archive_dir']}\")\n",
     "\n",

investing_algorithm_framework/__init__.py

@@ -29,7 +29,7 @@
     retag_backtests, migrate_backtests, \
     Blotter, DefaultBlotter, SimulationBlotter, Transaction, \
     SlippageModel, NoSlippage, PercentageSlippage, FixedSlippage, \
-    VolumeImpactSlippage, \
+    VolumeImpactSlippage, VolumeShareSlippage, FixedBasisPointsSlippage, \
     CommissionModel, NoCommission, PercentageCommission, FixedCommission, \
     FillModel, FullFill, VolumeBasedFill, \
     FXRateProvider, StaticFXRateProvider, \
@@ -258,6 +258,8 @@
     "PercentageSlippage",
     "FixedSlippage",
     "VolumeImpactSlippage",
+    "VolumeShareSlippage",
+    "FixedBasisPointsSlippage",
     "CommissionModel",
     "NoCommission",
     "PercentageCommission",