![](https://images.ecency.com/DQmXhGR5XFZ4vq8fuRPB91kxQ7D9JHi8taFn5b2aJ2mmb73/image.png) Since all the bots were coded with their own strategies. Im working on centralizing logic, and make the bots learn when they're trading. ``` # strategy.py """ Shared adaptive quoting and learning engine for all bots. Handles quoting, fill tracking, inventory-aware logic, and state persistence. """ import json import os import time class StrategyState: def __init__(self, state_file): self.state_file = state_file self.state = { 'buy_distance': 0.01, # as fraction of spread 'sell_distance': 0.01, 'min_profit': 0.005, 'inventory_target': 0.5, # 50% in token, 50% in HIVE 'order_stats': {}, # order_id: {status, submit_time, fill_time, cancel_time, side, price, size} 'fill_history': [], # [{order_id, side, price, size, fill_time, pnl}] 'cancel_history': [], 'realized_pnl': 0.0, 'unrealized_pnl': 0.0, 'last_update': time.time(), } self.load() def load(self): if os.path.exists(self.state_file): try: with open(self.state_file, 'r') as f: self.state.update(json.load(f)) except Exception: pass def save(self): try: with open(self.state_file, 'w') as f: json.dump(self.state, f, indent=2) except Exception: pass def track_order(self, order_id, side, price, size): self.state['order_stats'][order_id] = { 'status': 'open', 'submit_time': time.time(), 'side': side, 'price': price, 'size': size } self.save() def update_order_fill(self, order_id, fill_size, fill_price): order = self.state['order_stats'].get(order_id) if order: order['status'] = 'filled' order['fill_time'] = time.time() pnl = (fill_price - order['price']) * fill_size if order['side'] == 'buy' else (order['price'] - fill_price) * fill_size self.state['fill_history'].append({ 'order_id': order_id, 'side': order['side'], 'price': fill_price, 'size': fill_size, 'fill_time': order['fill_time'], 'pnl': pnl }) self.state['realized_pnl'] += pnl self.save() def update_order_cancel(self, order_id): order = self.state['order_stats'].get(order_id) if order: order['status'] = 'canceled' order['cancel_time'] = time.time() self.state['cancel_history'].append(order_id) self.save() def get_inventory_skew(self, token_balance, hive_balance, token_price): total_value = token_balance * token_price + hive_balance if total_value == 0: return 0.0 token_ratio = (token_balance * token_price) / total_value return token_ratio - self.state['inventory_target'] def get_adaptive_quote(self, best_bid, best_ask, spread, token_balance, hive_balance, token_price, volatility=0.0): # Inventory skew: if holding too much token, tighten sell, widen buy; vice versa skew = self.get_inventory_skew(token_balance, hive_balance, token_price) buy_dist = self.state['buy_distance'] + max(0, -skew) * 0.01 sell_dist = self.state['sell_distance'] + max(0, skew) * 0.01 # Volatility adjustment if volatility > 0.01: buy_dist += volatility * 0.5 sell_dist += volatility * 0.5 # Quote placement buy_quote = best_bid + spread * buy_dist sell_quote = best_ask - spread * sell_dist # Ensure min profit if sell_quote - buy_quote < self.state['min_profit'] * spread: mid = (best_bid + best_ask) / 2 buy_quote = mid - self.state['min_profit'] * spread / 2 sell_quote = mid + self.state['min_profit'] * spread / 2 return round(buy_quote, 8), round(sell_quote, 8) def get_order_ttl(self, liquidity, base_ttl=300): # Lower liquidity = longer patience if liquidity < 10: return base_ttl * 2 elif liquidity < 100: return base_ttl * 1.5 return base_ttl def should_cancel_order(self, order, best_bid, best_ask, max_age=1800): now = time.time() if order['status'] != 'open': return False age = now - order['submit_time'] # Cancel if too old if age > max_age: return True # Cancel if price is now far from best quote if order['side'] == 'buy' and order['price'] < best_bid * 0.98: return True if order['side'] == 'sell' and order['price'] > best_ask * 1.02: return True return False # Example usage: # state = StrategyState('.ltc_state.json') # buy_quote, sell_quote = state.get_adaptive_quote(...) # state.track_order(order_id, 'buy', buy_quote, size) # state.update_order_fill(order_id, fill_size, fill_price) # state.update_order_cancel(order_id) ``` ![](https://images.ecency.com/DQmeA6vzz3TQ8p7NUpypwx9Q8b5AHmEQR3F2ax5X4ehcNpB/image.png) --- ### 🪙 PeakeCoin Ecosystem --- **⚙️ HiveP.I.M.P. — Market Stabilization Layer** ::contentReference[oaicite:0]{index=0} Operated by **@hivepimp**, P.I.M.P. stabilizes **PEK markets** and supports liquidity on Hive Engine. Community participation strengthens long-term market health. 👉 https://peakecoin.info --- **🤖 PeakeBot — Autonomous Trading System (RC-AWARE)** ::contentReference[oaicite:1]{index=1} Independent multi-token trading bot featuring: - RC-aware execution - Adaptive delay logic - Self-regulating trade cycles 📊 Trading bot details: 👉 https://geocities.ws/p/e/peakecoin/trading-bot/peakebot_v0_01.html 💻 Open-source repositories: 👉 https://github.com/paulmoon410 --- **🎰 PeakeSino — The PeakeCoin Casino (Beta)** ::contentReference[oaicite:2]{index=2} Blockchain-powered games using **PEK** as the native in-game currency. Built on Hive with provable fairness and community-driven growth. 🃏 Play the beta games here: 👉 https://geocities.ws/peakecoin/pek_casino/beta_games/index.html --- ### 🙏 Acknowledgements Thanks to and please follow: **@enginewitty @ecoinstant @neoxian @txracer @thecrazygm @holdonia @aggroed** For their continued support, guidance, and help expanding the **PeakeCoin ecosystem**. ---

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