بنقرة واحدة
polymarket-trading
Polymarket-specific terminology, trading strategies, and API reference.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Polymarket-specific terminology, trading strategies, and API reference.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
SQLite best practices, connection management, and migration system for PolyFlup.
Chronological log of system improvements and session summaries for PolyFlup.
Coding standards, modular architecture, and common execution patterns for the PolyFlup Python backend.
Specialized in maintaining project documentation, ensuring it reflects the current state of the codebase and operational procedures.
Specialized in syncing the production database and performing analysis on trades, balances, and market history.
Specialized in syncing logs from production and analyzing them to find trade history, errors, or performance metrics.
| name | polymarket-trading |
| description | Polymarket-specific terminology, trading strategies, and API reference. |
The bot supports two confidence calculation methods for A/B testing:
Additive Method (Default):
Bayesian Method (Alternative):
evidence = (score - 0.5) × 2 # Scale to -1 to +1
log_LR = evidence × 3.0 × quality # Quality factor (0.7-1.5x)
log_odds += log_LR × weight
confidence = 1 / (1 + exp(-log_odds))
A/B Testing:
BAYESIAN_CONFIDENCE environment variable (default: NO)SELECT
AVG(CASE WHEN bias='UP' THEN edge ELSE -edge END) as avg_edge,
COUNT(*) as total,
SUM(CASE WHEN result='WIN' THEN 1 ELSE 0 END) as wins,
CAST(wins AS REAL) / COUNT(*) as win_rate
FROM trades
WHERE settled = 1
GROUP BY method;
SCALE_IN_TIME_LEFT: 450s (7.5 minutes)edge) and midpoint price:
edge >= 90% and price >= $0.80edge >= 80% and price >= $0.70edge >= 70% and price >= $0.65truncate_float(value, 2) for all order sizes to match exchange precisionfrom src.trading.orders import get_midpoint, get_spread, check_liquidity
# Get accurate midpoint price
price = get_midpoint(token_id)
# Check if liquidity is good
if check_liquidity(token_id, size=100, warn_threshold=0.05):
# Safe to trade
from src.trading.orders import place_batch_orders, place_market_order
# Batch Orders
results = place_batch_orders(orders)
# Market Sell
result = place_market_order(token_id, amount=10.0, side="SELL", order_type="FAK")