feat: Introduce Position Sizer functionality and enhance Backtester for multi-strategy support

- Added Position Sizer base class and implementations: AllInSizer, FixedFractionSizer, EqualWeightSizer, and VolatilityTargetSizer.
- Enhanced Backtester to support multiple strategies and introduced MultiBacktestResults for aggregated results.
- Updated strategy comparison metrics and visualizations to accommodate new multi-strategy features.
- Removed deprecated StrategyComparator utility and related tests, streamlining the comparison process.
- Added tests for multi-strategy backtesting and equal-weight portfolio creation.

Author: KarhouTam

docs/TUTORIAL_CN.md

@@ -24,7 +24,8 @@ data = loader.load_stock('600519', '20200101', '20231231')
 ### 2.2 运行回测
 
 ```python
-from quanteval import Backtester, DualMAStrategy
+from quanteval import Backtester
+from quanteval.strategies import DualMAStrategy
 
 strategy = DualMAStrategy(fast_window=10, slow_window=60)
 results = Backtester(strategy=strategy, data=data, transaction_costs=True).run()
@@ -34,23 +35,26 @@ print(results.summary())
 ### 2.3 策略对比
 
 ```python
-from quanteval import BollingerMeanReversionStrategy, StrategyComparator
+from quanteval import Backtester
+from quanteval.strategies import BollingerMeanReversionStrategy, DualMAStrategy
 
-comparison = StrategyComparator(
-    strategies=[
-        DualMAStrategy(5, 20),
-        BollingerMeanReversionStrategy(window=20, num_std=2.0),
-    ],
-    verbose=False,
-).compare(data)
+comparison = Backtester(
+    strategy={
+        'DualMA(5,20)': DualMAStrategy(5, 20),
+        'BollingerMR': BollingerMeanReversionStrategy(window=20, num_std=2.0),
+    },
+    data=data,
+    transaction_costs=False,
+).run()
 
 print(comparison.metrics_df)
 ```
 
 ### 2.4 参数优化
 
 ```python
-from quanteval import GridSearch
+from quanteval.optimization import GridSearch
+from quanteval.strategies import DualMAStrategy
 
 search = GridSearch(
     DualMAStrategy,
@@ -69,7 +73,8 @@ print(result.best_params)
 ### 2.5 Walk-forward 样本外验证
 
 ```python
-from quanteval import WalkForwardAnalysis
+from quanteval.optimization import WalkForwardAnalysis
+from quanteval.strategies import DualMAStrategy
 
 wfa = WalkForwardAnalysis(
     DualMAStrategy,

docs/TUTORIAL_EN.md

@@ -24,7 +24,8 @@ data = loader.load_stock('600519', '20200101', '20231231')
 ### 2.2 Running Backtests
 
 ```python
-from quanteval import Backtester, DualMAStrategy
+from quanteval import Backtester
+from quanteval.strategies import DualMAStrategy
 
 strategy = DualMAStrategy(fast_window=10, slow_window=60)
 results = Backtester(strategy=strategy, data=data, transaction_costs=True).run()
@@ -34,23 +35,26 @@ print(results.summary())
 ### 2.3 Strategy Comparison
 
 ```python
-from quanteval import BollingerMeanReversionStrategy, StrategyComparator
+from quanteval import Backtester
+from quanteval.strategies import BollingerMeanReversionStrategy, DualMAStrategy
 
-comparison = StrategyComparator(
-    strategies=[
-        DualMAStrategy(5, 20),
-        BollingerMeanReversionStrategy(window=20, num_std=2.0),
-    ],
-    verbose=False,
-).compare(data)
+comparison = Backtester(
+    strategy={
+        'DualMA(5,20)': DualMAStrategy(5, 20),
+        'BollingerMR': BollingerMeanReversionStrategy(window=20, num_std=2.0),
+    },
+    data=data,
+    transaction_costs=False,
+).run()
 
 print(comparison.metrics_df)
 ```
 
 ### 2.4 Parameter Optimization
 
 ```python
-from quanteval import GridSearch
+from quanteval.optimization import GridSearch
+from quanteval.strategies import DualMAStrategy
 
 search = GridSearch(
     DualMAStrategy,
@@ -69,7 +73,8 @@ print(result.best_params)
 ### 2.5 Walk-forward Out-of-sample Validation
 
 ```python
-from quanteval import WalkForwardAnalysis
+from quanteval.optimization import WalkForwardAnalysis
+from quanteval.strategies import DualMAStrategy
 
 wfa = WalkForwardAnalysis(
     DualMAStrategy,

examples/03_strategy_comparison.ipynb

@@ -22,12 +22,7 @@
    "id": "1",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "from quanteval import BollingerMeanReversionStrategy, DualMAStrategy, StrategyComparator"
-   ]
+   "source": "import numpy as np\nimport pandas as pd\n\nfrom quanteval import Backtester\nfrom quanteval.strategies import BollingerMeanReversionStrategy, DualMAStrategy"
   },
   {
    "cell_type": "code",
@@ -67,15 +62,7 @@
    "id": "4",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "comparator = StrategyComparator(\n",
-    "    strategies=[DualMAStrategy(5, 20), BollingerMeanReversionStrategy(20, 2.0)],\n",
-    "    strategy_names=['DualMA(5,20)', 'BollingerMR'],\n",
-    "    verbose=False,\n",
-    ")\n",
-    "comparison = comparator.compare(data)\n",
-    "comparison.metrics_df"
-   ]
+   "source": "comparison = Backtester(\n    strategy={\n        'DualMA(5,20)': DualMAStrategy(5, 20),\n        'BollingerMR': BollingerMeanReversionStrategy(20, 2.0),\n    },\n    data=data,\n    transaction_costs=False,\n).run()\ncomparison.metrics_df"
   },
   {
    "cell_type": "markdown",
@@ -97,10 +84,7 @@
    "id": "6",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "portfolio = comparator.create_equal_weight_portfolio(data)\n",
-    "portfolio.summary()"
-   ]
+   "source": "portfolio = comparison.create_equal_weight_portfolio()\nportfolio.summary()"
   }
  ],
  "metadata": {