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Extract magic numbers into named constants

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Define module-level constants for market activity, volatility, and liquidity
thresholds. This improves code readability and makes threshold values easier
to maintain and adjust in the future.

Constants added:
- ACTIVITY_VERY_HIGH_THRESHOLD, ACTIVITY_HIGH_THRESHOLD, etc.
- VOLATILITY_VERY_VOLATILE_THRESHOLD, VOLATILITY_VOLATILE_THRESHOLD, etc.
- LIQUIDITY_VERY_HIGH_THRESHOLD, LIQUIDITY_HIGH_THRESHOLD, etc.

Addresses PR #2 review comments on lines 1084 and 1224.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
Nitzan Pomerantz
2025-10-24 19:22:03 +03:00
parent 2968711307
commit 11512045dd
1 changed files with 43 additions and 25 deletions
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nadlan_mcp/govmap.py
+43 -25
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@@ -11,6 +11,24 @@ from nadlan_mcp.config import GovmapConfig, get_config
logger = logging.getLogger(__name__)
# Market activity score thresholds (deals per month)
ACTIVITY_VERY_HIGH_THRESHOLD = 10
ACTIVITY_HIGH_THRESHOLD = 5
ACTIVITY_MODERATE_THRESHOLD = 3
ACTIVITY_LOW_THRESHOLD = 1
# Price volatility thresholds (coefficient of variation %)
VOLATILITY_VERY_VOLATILE_THRESHOLD = 50
VOLATILITY_VOLATILE_THRESHOLD = 30
VOLATILITY_MODERATE_THRESHOLD = 20
VOLATILITY_STABLE_THRESHOLD = 10
# Market liquidity thresholds (deals per month)
LIQUIDITY_VERY_HIGH_THRESHOLD = 8
LIQUIDITY_HIGH_THRESHOLD = 5
LIQUIDITY_MODERATE_THRESHOLD = 2
LIQUIDITY_LOW_THRESHOLD = 0.5
class GovmapClient:
"""
@@ -1066,18 +1084,18 @@ class GovmapClient:
deals_per_month = total_deals / unique_months if unique_months > 0 else 0
# Calculate activity score (0-100)
# Based on deals per month: 0-1 = very low, 1-3 = low, 3-5 = moderate, 5-10 = high, 10+ = very high
if deals_per_month >= 10:
# Based on deals per month using defined thresholds
if deals_per_month >= ACTIVITY_VERY_HIGH_THRESHOLD:
activity_score = 100
activity_level = "very_high"
elif deals_per_month >= 5:
activity_score = 75 + ((deals_per_month - 5) / 5) * 25
elif deals_per_month >= ACTIVITY_HIGH_THRESHOLD:
activity_score = 75 + ((deals_per_month - ACTIVITY_HIGH_THRESHOLD) / ACTIVITY_HIGH_THRESHOLD) * 25
activity_level = "high"
elif deals_per_month >= 3:
activity_score = 50 + ((deals_per_month - 3) / 2) * 25
elif deals_per_month >= ACTIVITY_MODERATE_THRESHOLD:
activity_score = 50 + ((deals_per_month - ACTIVITY_MODERATE_THRESHOLD) / (ACTIVITY_HIGH_THRESHOLD - ACTIVITY_MODERATE_THRESHOLD)) * 25
activity_level = "moderate"
elif deals_per_month >= 1:
activity_score = 25 + ((deals_per_month - 1) / 2) * 25
elif deals_per_month >= ACTIVITY_LOW_THRESHOLD:
activity_score = 25 + ((deals_per_month - ACTIVITY_LOW_THRESHOLD) / (ACTIVITY_MODERATE_THRESHOLD - ACTIVITY_LOW_THRESHOLD)) * 25
activity_level = "low"
else:
activity_score = deals_per_month * 25
@@ -1206,21 +1224,21 @@ class GovmapClient:
coefficient_of_variation = 0
# Convert CV to volatility score (0-100, lower is better)
# CV < 10% = very stable, 10-20% = stable, 20-30% = moderate, 30-50% = volatile, >50% = very volatile
if coefficient_of_variation > 50:
# Using defined volatility thresholds
if coefficient_of_variation > VOLATILITY_VERY_VOLATILE_THRESHOLD:
volatility_score = 100
market_stability = "very_volatile"
elif coefficient_of_variation > 30:
volatility_score = 75 + ((coefficient_of_variation - 30) / 20) * 25
elif coefficient_of_variation > VOLATILITY_VOLATILE_THRESHOLD:
volatility_score = 75 + ((coefficient_of_variation - VOLATILITY_VOLATILE_THRESHOLD) / (VOLATILITY_VERY_VOLATILE_THRESHOLD - VOLATILITY_VOLATILE_THRESHOLD)) * 25
market_stability = "volatile"
elif coefficient_of_variation > 20:
volatility_score = 50 + ((coefficient_of_variation - 20) / 10) * 25
elif coefficient_of_variation > VOLATILITY_MODERATE_THRESHOLD:
volatility_score = 50 + ((coefficient_of_variation - VOLATILITY_MODERATE_THRESHOLD) / (VOLATILITY_VOLATILE_THRESHOLD - VOLATILITY_MODERATE_THRESHOLD)) * 25
market_stability = "moderate"
elif coefficient_of_variation > 10:
volatility_score = 25 + ((coefficient_of_variation - 10) / 10) * 25
elif coefficient_of_variation > VOLATILITY_STABLE_THRESHOLD:
volatility_score = 25 + ((coefficient_of_variation - VOLATILITY_STABLE_THRESHOLD) / (VOLATILITY_MODERATE_THRESHOLD - VOLATILITY_STABLE_THRESHOLD)) * 25
market_stability = "stable"
else:
volatility_score = (coefficient_of_variation / 10) * 25
volatility_score = (coefficient_of_variation / VOLATILITY_STABLE_THRESHOLD) * 25
market_stability = "very_stable"
# Calculate investment score (0-100)
@@ -1322,18 +1340,18 @@ class GovmapClient:
deals_per_quarter = total_deals / unique_quarters if unique_quarters > 0 else 0
# Calculate velocity score (similar to activity score but focused on turnover)
# Based on monthly deal velocity
if deals_per_month >= 8:
# Based on monthly deal velocity using defined thresholds
if deals_per_month >= LIQUIDITY_VERY_HIGH_THRESHOLD:
velocity_score = 100
liquidity_rating = "very_high"
elif deals_per_month >= 5:
velocity_score = 75 + ((deals_per_month - 5) / 3) * 25
elif deals_per_month >= LIQUIDITY_HIGH_THRESHOLD:
velocity_score = 75 + ((deals_per_month - LIQUIDITY_HIGH_THRESHOLD) / (LIQUIDITY_VERY_HIGH_THRESHOLD - LIQUIDITY_HIGH_THRESHOLD)) * 25
liquidity_rating = "high"
elif deals_per_month >= 2:
velocity_score = 50 + ((deals_per_month - 2) / 3) * 25
elif deals_per_month >= LIQUIDITY_MODERATE_THRESHOLD:
velocity_score = 50 + ((deals_per_month - LIQUIDITY_MODERATE_THRESHOLD) / (LIQUIDITY_HIGH_THRESHOLD - LIQUIDITY_MODERATE_THRESHOLD)) * 25
liquidity_rating = "moderate"
elif deals_per_month >= 0.5:
velocity_score = 25 + ((deals_per_month - 0.5) / 1.5) * 25
elif deals_per_month >= LIQUIDITY_LOW_THRESHOLD:
velocity_score = 25 + ((deals_per_month - LIQUIDITY_LOW_THRESHOLD) / (LIQUIDITY_MODERATE_THRESHOLD - LIQUIDITY_LOW_THRESHOLD)) * 25
liquidity_rating = "low"
else:
velocity_score = deals_per_month * 50