Minimum Volume Filter

app.py

@@ -54,6 +54,7 @@ class SimulationParams(BaseModel):
     minProbCurrent: float
     daysBefore: int
     investmentProbability: float
+    minVolume: Optional[float] = 1000000  # Default 1 million
     targetReturn: Optional[float] = None
     numFunds: Optional[int] = None
     kellyFraction: Optional[float] = None
@@ -116,6 +117,7 @@ async def run_simulation(params: SimulationParams):
                 min_prob_current=params.minProbCurrent,
                 investment_probability=params.investmentProbability,
                 target_return=params.targetReturn,
+                min_volume=params.minVolume,
                 random_seed=seed_offset + i
             )
             
@@ -154,6 +156,7 @@ async def run_simulation(params: SimulationParams):
                 investment_probability=params.investmentProbability,
                 kelly_fraction=params.kellyFraction or 0.5,
                 edge_estimate=params.edgeEstimate or 'historical',
+                min_volume=params.minVolume,
                 random_seed=seed_offset + i
             )
 
@@ -189,6 +192,7 @@ async def run_simulation(params: SimulationParams):
                 min_prob_current=params.minProbCurrent,
                 investment_probability=params.investmentProbability,
                 target_return=params.targetReturn,
+                min_volume=params.minVolume,
                 random_seed=seed_offset + i
             )
 

simulation_utils.py

@@ -221,7 +221,7 @@ def calculate_investment_return(market: pd.Series, days_before: int, capital: fl
     else:  # Market resolved False (safe bet lost)
         return 0.0
 
-def run_single_fund_simulation_fast(df: pd.DataFrame, 
+def run_single_fund_simulation_fast(df: pd.DataFrame,
                                    starting_capital: float = 10000,
                                    start_date: str = '2025-01-01',
                                    max_duration_days: int = 365,
@@ -230,6 +230,7 @@ def run_single_fund_simulation_fast(df: pd.DataFrame,
                                    min_prob_current: float = 0.90,
                                    investment_probability: float = 0.5,
                                    target_return: Optional[float] = None,
+                                   min_volume: Optional[float] = None,
                                    random_seed: Optional[int] = None) -> Dict[str, Any]:
     """
     Optimized single fund simulation using pre-indexed markets and event-driven approach.
@@ -237,10 +238,10 @@ def run_single_fund_simulation_fast(df: pd.DataFrame,
     random_state = np.random.RandomState(random_seed)
     start_dt = pd.to_datetime(start_date, utc=True)
     end_dt = start_dt + timedelta(days=max_duration_days)
-    
+
     # Pre-filter and index markets by trading date for massive speedup
     prob_col = f'probability{days_before}d'
-    
+
     # Filter eligible markets once upfront
     market_mask = (
         (df['probability7d'] >= min_prob_7d) &
@@ -251,6 +252,10 @@ def run_single_fund_simulation_fast(df: pd.DataFrame,
         (df['closingDate'] >= start_dt + timedelta(days=days_before)) &
         (df['closingDate'] <= end_dt + timedelta(days=days_before))
     )
+
+    # Add volume filter if specified
+    if min_volume is not None and 'volume' in df.columns:
+        market_mask = market_mask & (df['volume'] >= min_volume)
     
     eligible_markets = df[market_mask].copy()
     if len(eligible_markets) == 0:
@@ -273,10 +278,11 @@ def run_single_fund_simulation_fast(df: pd.DataFrame,
                 'min_prob_current': min_prob_current,
                 'investment_probability': investment_probability,
                 'target_return': target_return,
+                'min_volume': min_volume,
                 'random_seed': random_seed
             }
         }
-    
+
     # Calculate trading dates for each market (days_before days before resolution)
     eligible_markets['trading_date'] = eligible_markets['closingDate'] - timedelta(days=days_before)
     
@@ -397,12 +403,13 @@ def run_single_fund_simulation_fast(df: pd.DataFrame,
             'min_prob_current': min_prob_current,
             'investment_probability': investment_probability,
             'target_return': target_return,
+            'min_volume': min_volume,
             'random_seed': random_seed
         }
     }
 
 # Keep original function for backwards compatibility
-def run_single_fund_simulation(df: pd.DataFrame, 
+def run_single_fund_simulation(df: pd.DataFrame,
                               starting_capital: float = 10000,
                               start_date: str = '2025-01-01',
                               max_duration_days: int = 365,
@@ -411,13 +418,14 @@ def run_single_fund_simulation(df: pd.DataFrame,
                               min_prob_current: float = 0.90,
                               investment_probability: float = 0.5,
                               target_return: Optional[float] = None,
+                              min_volume: Optional[float] = None,
                               random_seed: Optional[int] = None) -> Dict[str, Any]:
     """
     Run a single fund simulation with day-by-day investment decisions.
-    
+
     Each day (when not already invested), the trader decides to invest with
     probability alpha, then selects uniformly at random from available markets.
-    
+
     Args:
         df: Market data DataFrame
         starting_capital: Initial capital
@@ -428,8 +436,9 @@ def run_single_fund_simulation(df: pd.DataFrame,
         min_prob_current: Minimum probability at investment day
         investment_probability: Probability of investing on any given day (alpha)
         target_return: Target return threshold to stop trading (None = no threshold)
+        min_volume: Minimum market volume to consider (None = no filter)
         random_seed: Random seed for reproducibility
-    
+
     Returns:
         Dictionary containing simulation results
     """
@@ -444,6 +453,7 @@ def run_single_fund_simulation(df: pd.DataFrame,
         min_prob_current=min_prob_current,
         investment_probability=investment_probability,
         target_return=target_return,
+        min_volume=min_volume,
         random_seed=random_seed
     )
 
@@ -518,6 +528,7 @@ def run_kelly_simulation(df: pd.DataFrame,
                         investment_probability: float = 0.5,
                         kelly_fraction: float = 0.5,
                         edge_estimate: str = 'historical',
+                        min_volume: Optional[float] = None,
                         random_seed: Optional[int] = None) -> Dict[str, Any]:
     """
     Run a simulation using Kelly criterion for position sizing.
@@ -536,6 +547,7 @@ def run_kelly_simulation(df: pd.DataFrame,
         investment_probability: Probability of attempting to invest on any given day
         kelly_fraction: Fraction of Kelly to use (0.5 = half Kelly, 1.0 = full Kelly)
         edge_estimate: Method to estimate edge ('historical', 'fixed_edge', 'fixed_edge_2')
+        min_volume: Minimum market volume to consider (None = no filter)
         random_seed: Random seed for reproducibility
 
     Returns:
@@ -558,6 +570,10 @@ def run_kelly_simulation(df: pd.DataFrame,
         (df['closingDate'] <= end_dt + timedelta(days=days_before))
     )
 
+    # Add volume filter if specified
+    if min_volume is not None and 'volume' in df.columns:
+        market_mask = market_mask & (df['volume'] >= min_volume)
+
     eligible_markets = df[market_mask].copy()
 
     if len(eligible_markets) == 0:
@@ -586,6 +602,7 @@ def run_kelly_simulation(df: pd.DataFrame,
                 'investment_probability': investment_probability,
                 'kelly_fraction': kelly_fraction,
                 'edge_estimate': edge_estimate,
+                'min_volume': min_volume,
                 'random_seed': random_seed
             }
         }
@@ -751,6 +768,7 @@ def run_kelly_simulation(df: pd.DataFrame,
             'investment_probability': investment_probability,
             'kelly_fraction': kelly_fraction,
             'edge_estimate': edge_estimate,
+            'min_volume': min_volume,
             'random_seed': random_seed
         }
     }
@@ -920,12 +938,13 @@ def run_multi_fund_simulation(df: pd.DataFrame,
                              min_prob_current: float = 0.90,
                              investment_probability: float = 0.5,
                              target_return: Optional[float] = None,
+                             min_volume: Optional[float] = None,
                              random_seed: Optional[int] = None) -> Dict[str, Any]:
     """
     Run a multi-fund simulation where capital is divided into independent funds.
-    
+
     Each fund operates independently with the same investment probability (alpha).
-    
+
     Args:
         df: Market data DataFrame
         n_funds: Number of independent funds to create
@@ -937,8 +956,9 @@ def run_multi_fund_simulation(df: pd.DataFrame,
         min_prob_current: Minimum probability at investment day
         investment_probability: Probability of investing on any given day (alpha)
         target_return: Target return threshold per fund (None = no threshold)
+        min_volume: Minimum market volume to consider (None = no filter)
         random_seed: Random seed for reproducibility
-    
+
     Returns:
         Dictionary containing multi-fund simulation results
     """
@@ -967,6 +987,7 @@ def run_multi_fund_simulation(df: pd.DataFrame,
             min_prob_current=min_prob_current,
             investment_probability=investment_probability,
             target_return=target_return,
+            min_volume=min_volume,
             random_seed=fund_seed
         )
         
@@ -1029,6 +1050,7 @@ def run_multi_fund_simulation(df: pd.DataFrame,
             'min_prob_current': min_prob_current,
             'investment_probability': investment_probability,
             'target_return': target_return,
+            'min_volume': min_volume,
             'random_seed': random_seed
         }
     }

static/index.html

@@ -4,7 +4,7 @@
     <meta charset="UTF-8">
     <meta name="viewport" content="width=device-width, initial-scale=1.0">
     <title>Safe Choices - Prediction Market Simulation</title>
-    <link rel="stylesheet" href="/static/styles.css?v=10">
+    <link rel="stylesheet" href="/static/styles.css?v=11">
     <link rel="preconnect" href="https://fonts.googleapis.com">
     <link rel="preconnect" href="https://fonts.gstatic.com" crossorigin>
     <link href="https://fonts.googleapis.com/css2?family=Inter:wght@400;500;600;700&display=swap" rel="stylesheet">
@@ -203,6 +203,17 @@
                             </div>
                         </div>
 
+                        <div class="param-group">
+                            <label class="param-label">
+                                Min Volume
+                                <span class="param-hint" data-tooltip="Minimum market volume in USD">?</span>
+                            </label>
+                            <div class="param-input-wrap">
+                                <input type="number" id="minVolume" class="param-input" value="1000000" min="100000" max="10000000" step="100000">
+                                <span class="param-unit">$</span>
+                            </div>
+                        </div>
+
                         <div class="param-group">
                             <label class="param-label">
                                 Trading Frequency
@@ -534,6 +545,6 @@
         </div>
     </main>
 
-    <script src="/static/script.js?v=10"></script>
+    <script src="/static/script.js?v=11"></script>
 </body>
 </html>

static/script.js

@@ -170,7 +170,8 @@ function getSimulationParameters() {
         minProb7d: parseFloat(document.getElementById('minProb7d').value) / 100,
         minProbCurrent: parseFloat(document.getElementById('minProbCurrent').value) / 100,
         daysBefore: parseInt(document.getElementById('daysBefore').value),
-        investmentProbability: parseFloat(document.getElementById('investmentProbability').value)
+        investmentProbability: parseFloat(document.getElementById('investmentProbability').value),
+        minVolume: parseFloat(document.getElementById('minVolume').value)
     };
 
     if (currentSimType === 'threshold') {
@@ -212,7 +213,8 @@ async function callSimulationAPI(params) {
         minProb7d: params.minProb7d,
         minProbCurrent: params.minProbCurrent,
         daysBefore: params.daysBefore,
-        investmentProbability: params.investmentProbability
+        investmentProbability: params.investmentProbability,
+        minVolume: params.minVolume
     };
 
     if (params.targetReturn !== undefined) {