feat: Telegram bot + 6-strategy auto-trader for Polymarket#207
feat: Telegram bot + 6-strategy auto-trader for Polymarket#207felipecolombarolli-cmyk wants to merge 13 commits intoPolymarket:mainfrom
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Add a full Telegram bot interface and autonomous trading system for Polymarket, with multiple fixes and enhancements: **New files:** - `scripts/python/telegram_bot.py` - Telegram bot with Portuguese NLP, smart_bet (auto-execute), balance checks, and goal-based auto-trading - `scripts/python/auto_trader.py` - Autonomous trader with arbitrage scanning (YES+NO < $1), aggressive order execution, and configurable strategies via LLM (supports Claude > xAI/Grok > OpenAI) - `scripts/python/agent.py` - LLM-powered agent with market search, keyword expansion, and proxy wallet support **Fixes:** - Fix Gamma API market search (text_query broken): fetch top 500 markets by volume and filter locally with keyword scoring - Fix stuck/unfilled orders: add aggressive order execution that reads orderbook and crosses the spread for immediate fills - Fix Polygon RPC (polygon-rpc.com dead/401): replace with polygon-bor-rpc.publicnode.com across all files - Fix USDC-to-shares conversion in order execution **Enhancements:** - Add execute_aggressive_order() and execute_market_buy() (FOK) to polymarket.py for instant order fills - Add search_markets() to gamma.py for local keyword-based filtering - Add multi-LLM provider support (Anthropic Claude > xAI Grok > OpenAI) - Add arbitrage opportunity scanner in auto-trader context gathering - Update .env.example with Telegram and xAI configuration - Add langchain-anthropic to requirements.txt Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add a dedicated strategy engine that implements the 6 dominant strategies used by the most profitable Polymarket bots (including the $313→$414K latency arbitrage bot with 98% win rate): 1. Temporal/Latency Arbitrage: Monitor BTC/ETH/SOL prices from Binance in real-time, detect momentum in 15-min crypto markets 2. Parity Arbitrage: Enhanced YES+NO < $0.97 with net profit after fees 3. Systematic NO Bias: 70% of markets resolve NO, target overhyped ones 4. High-Probability Auto-Compounding: 92-98% outcomes for safe returns 5. Long-Shot Floor Buying: 1-5 cent outcomes with 20x-100x upside 6. Portfolio Management: Stop-loss, diversification New: scripts/python/strategies.py (CryptoPriceMonitor + StrategyEngine) Updated: scripts/python/auto_trader.py (strategy integration, faster cycles) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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| score += 1 # More room for NO profit | ||
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| if score >= 2: | ||
| expected_return = (1.0 / no_price - 1.0) * 0.7 # 70% NO resolution rate |
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NO bias expected return formula overstates by 30 percentage points
High Severity
The expected_return formula (1.0 / no_price - 1.0) * 0.7 is mathematically wrong. It computes 0.7/P - 0.7 but the correct expected return accounting for the 30% loss is 0.7/P - 1.0. This always overstates returns by exactly 30 percentage points. For example, at no_price=0.85, the code reports +12.4% expected return but the true expected return is −17.6%. This inflated figure is displayed in the reasoning field sent to the LLM, which could cause the auto-trader to execute negative expected-value trades with real money.
| "question": m.get("question", ""), | ||
| "no_price": no_price, | ||
| "yes_price": yes_price, | ||
| "no_token_id": token_ids[1], |
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Strategy signals use inconsistent token_id key names
Medium Severity
The NO_BIAS strategy uses no_token_id and PARITY_ARB uses yes_token_id/no_token_id in their signal dictionaries, while the auto-trader's LLM summary extraction at s.get('token_id') only looks for the key token_id. This means the LLM's explicit strategy summary never includes token IDs for NO_BIAS or PARITY_ARB signals, making it harder for the LLM to act on these strategies. The LATENCY_ARB, HIGH_PROB, and LONGSHOT strategies correctly use token_id.
Additional Locations (2)
| """Check if the user is authorized. If no allowlist is set, allow everyone.""" | ||
| if not ALLOWED_USERS: | ||
| return True | ||
| return user_id in ALLOWED_USERS |
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Telegram bot allows all users by default
High Severity
When TELEGRAM_ALLOWED_USERS is empty (the default in .env.example), is_authorized returns True for every Telegram user. Since this bot can execute real-money trades, view balances, and manage a Polymarket wallet, any Telegram user who discovers the bot's username can fully control the trading account. A deny-by-default approach would be safer for a financial bot.
| else: | ||
| self._pending_orders.pop(user_id, None) | ||
| await update.message.reply_text("Order cancelled.") | ||
| return |
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Telegram text confirmation missing Portuguese words causes accidental cancellation
Medium Severity
The Telegram bot's text-based order confirmation only accepts English words ("yes", "y", "confirm"), while the CLI agent also accepts Portuguese ("sim", "s", "confirma"). Since the bot interface and system prompt are in Portuguese, a user typing "sim" to confirm a pending buy/sell order would have their order silently cancelled instead — the else branch pops and discards it.
| logger.info(f"AutoTrader using OpenAI: {model}") | ||
| return ChatOpenAI(model=model, temperature=0.3) | ||
| else: | ||
| raise ValueError("No LLM API key. Set ANTHROPIC_API_KEY, XAI_API_KEY, or OPENAI_API_KEY.") |
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Duplicate _build_llm functions across agent and auto-trader
Low Severity
_build_llm() in agent.py and auto_trader.py are nearly identical — same provider priority (Anthropic > xAI > OpenAI), same env var lookups, same model defaults — differing only in temperature (0 vs 0.3). A third variant _build_executor_llm() exists in executor.py. Any change to provider support or defaults needs to be replicated across all three locations.
Additional Locations (1)
| size=order["amount"], | ||
| side=order["side"], | ||
| token_id=order["token_id"], | ||
| ) |
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CLI order execution treats dollar amount as share count
Medium Severity
In confirm_pending_order, order["amount"] (displayed as Valor: ${amount} — a USDC dollar value) is passed as the size parameter to execute_order, where size means number of shares. A user confirming a $10 buy at price $0.50 expects to spend $10 (~20 shares), but the code creates an order for 10 shares at $0.50 = $6.50. The Telegram path correctly handles this via execute_aggressive_order which converts USDC to shares, making the two execution paths inconsistent.
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| trader.set_dry_run(True) | ||
| await update.message.reply_text("🔄 Running analysis cycle (dry run)...") | ||
| await trader.run_cycle() |
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Ad-hoc trade commands corrupt running auto-trader dry_run state
Medium Severity
/trade_now, /autotrade, and the confirm_live_cycle callback all call set_dry_run() on the shared AutoTrader instance without saving or restoring the previous state. If the auto-trader is already running in LIVE mode, sending /trade_now permanently switches it to dry-run. Similarly, confirm_live_cycle forces set_dry_run(True) after its cycle, silently converting a running live session into dry-run. The user receives no notification of this state change.
Additional Locations (2)
| # Auto-Trading Settings | ||
| AUTOTRADE_INTERVAL_MIN="30" # Minutes between trade cycles | ||
| AUTOTRADE_MAX_AMOUNT="25" # Max USDC per single trade | ||
| AUTOTRADE_MAX_TRADES="3" # Max trades per cycle |
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Config defaults in .env.example don't match code fallbacks
Low Severity
.env.example documents AUTOTRADE_INTERVAL_MIN="30" and AUTOTRADE_MAX_TRADES="3", but the code fallback defaults are "10" and "5" respectively. A user who doesn't copy these values into their .env gets a 3x more frequent trading interval and 67% more trades per cycle than the documented behavior suggests, leading to more aggressive trading than expected in a financial application.
Additional Locations (1)
- New trade_db.py: SQLite persistence for trades, positions, strategy performance. Auto-learning adjusts strategy weights (win +10%, loss -15%) - Rewritten auto_trader.py: Dual-speed loop (fast 30s + deep 5min cycles), PositionManager with stop-loss/take-profit/trailing stop, direct execution for obvious trades (arbs >2%, latency arb >0.9 confidence) without LLM - Updated strategies.py: 7 cryptos (added DOGE, XRP, AVAX, LINK), spike detection (>1%/1min), market regime detection (trending/volatile/ranging), dynamic strategy weighting from auto-learning data - Updated telegram_bot.py: New commands /stats, /strategies, /speed, /stoploss, /takeprofit for performance dashboard and configuration Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
| continue # strategy disabled by auto-learning | ||
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| token_id = sig.get("token_id", "") | ||
| if token_id: |
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Parity arbitrage auto-execution uses wrong key name
High Severity
_execute_obvious_trades reads sig.get("token_id", "") for parity arbitrage signals, but scan_parity_arbitrage in strategies.py produces signals with yes_token_id and no_token_id — never token_id. The lookup always returns "", and the subsequent if token_id: check always fails, meaning parity arbitrage opportunities are silently never auto-executed.
Additional Locations (1)
| f"Portfólio: ${portfolio_value:.2f} >= Meta: ${goal_amount:.2f}" | ||
| ) | ||
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| return await self.start() |
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Goal-based trading silently ignores user's interval setting
Medium Severity
start_with_goal accepts an interval_min parameter that is threaded through from the user's request all the way to this method, but the value is never applied to self.fast_interval_sec or any other timing config. The confirmation UI at line 610 even displays "⏱ Intervalo: a cada {interval_min} min" to the user, confirming their chosen interval, but the auto-trader runs at its default cadence instead. The user's requested cycle timing is silently discarded.
Additional Locations (1)
Replace trader.interval_minutes (removed in v2) with trader.fast_interval_sec and trader.deep_interval_cycles in cmd_autotrade_live confirmation message. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add _dead_markets set that caches token_ids whose orderbook no longer exists. Positions in dead markets are skipped in _manage_positions() and 404 errors in _execute_trade() automatically blacklist the market. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Add balance-aware bet sizing that automatically scales trade amounts: $100+ → up to $25/trade (Full) $50+ → up to $10/trade (Medium) $20+ → up to $5/trade (Small) $5+ → up to $2/trade (Micro) $1+ → up to $0.50/trade (Survival) <$1 → no trading Also caps single trade at 40% of balance, updates LLM prompt with current tier info, and shows tier in /trade_status. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
When USDC balance < $2, automatically sell up to 2 positions to free up capital. Prioritizes profitable positions first, then highest value. Skips dead markets and positions worth < $0.50. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
The previous check `not exit_trades` was always False because dead market stop-losses (which always fail with 404) were counted as exits. Now only counts real exits from active markets. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
| return ( | ||
| f"🎯 Meta já atingida!\n" | ||
| f"Portfólio: ${portfolio_value:.2f} >= Meta: ${goal_amount:.2f}" | ||
| ) |
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start_with_goal mutates config before early-return check
Medium Severity
start_with_goal unconditionally overwrites max_trade_amount, max_trades_per_cycle, dry_run, and goal_mode before checking if the goal is already met. If the goal is already reached, it returns early but leaves these values permanently mutated. A subsequent /autotrade call would use the reduced start_amount as max_trade_amount and be stuck with 5 max_trades_per_cycle. Neither stop() nor any other method restores these original config values.
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| size = round(amount / price, 2) | ||
| if size < 5: | ||
| size = 5 |
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Minimum order size bypasses balance-based safety caps
Medium Severity
execute_aggressive_order enforces a minimum of 5 shares (if size < 5: size = 5), which can silently cause the actual USDC spent to far exceed the amount the auto-trader carefully computed via _adjust_amount_for_balance. For instance, with a $5 balance capped to a $2 bet on a $0.95 outcome, the size (2.1 shares) gets forced to 5 shares, spending $4.75 — more than double the intended cap and 95% of the balance.
Additional Locations (1)
| markets.append(formatted_market_data) | ||
| if self.polymarket: | ||
| formatted_market_data = self.polymarket.map_api_to_market(market_data) | ||
| markets.append(formatted_market_data) |
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Unguarded self.polymarket access causes NoneType crash
High Severity
format_trade_prompt_for_execution calls self.polymarket.get_usdc_balance() without a None check. The commit changed self.polymarket from always-initialized to conditionally None (when POLYGON_WALLET_PRIVATE_KEY is unset), but this method wasn't updated. When called via trade.py's one_best_trade, it will crash with an AttributeError on NoneType.
- Auto-restart: loop restarts on crash with 30s backoff (up to 50x) - Timeouts on all network calls: balance (10s), positions (15s), markets (15s), strategy (20s), context (30s), LLM (60s) - Non-blocking notifications: Telegram send_message capped at 10s - Prevents loop from dying silently when API calls hang Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
| f"Resultado: {result}" | ||
| ) | ||
| except Exception as e: | ||
| return f"❌ Erro ao executar {order.get('side', '').lower()}: {e}" |
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Blocking synchronous I/O in async Telegram handlers
Medium Severity
_execute_order is a synchronous method that makes blocking HTTP calls (via execute_aggressive_order, which reads the orderbook and posts an order). It's called directly from async handlers like handle_message and handle_callback without run_in_executor. This blocks the asyncio event loop, making the Telegram bot unresponsive during trade execution. The auto_trader.py correctly wraps the same call with loop.run_in_executor.
Additional Locations (1)
…les) - gamma.py: add timeout=10 to httpx.get() (was hanging indefinitely) - auto_trader.py: remove duplicate Gamma API call in _gather_context_sync (same call was made twice, now reuses raw_markets) - auto_trader.py: add timeout=10 to all httpx.get() position fetches - auto_trader.py: add timeout=10 to Web3 HTTPProvider for RPC calls Expected cycle times: deep ~60s (was 30min), fast ~15s (was 15min) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Start LIVE trading immediately when /autotrade_live is sent, without requiring inline button confirmation. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
| if action == "BUY": | ||
| size = round(amount / price, 2) | ||
| if size < 5: | ||
| size = 5 |
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Minimum order size bypasses balance safety caps
Medium Severity
After _adjust_amount_for_balance carefully caps the USDC amount (e.g., $0.50 in survival tier), the if size < 5: size = 5 minimum silently inflates the actual spend. If amount=$0.50 and price=$0.10, size goes from 5.0 (OK) to 5.0, but at price=$0.50, size goes from 1.0 to 5.0, spending $2.50 instead of $0.50 — violating the 40% balance safety cap and tier limits.
Additional Locations (1)
| return f"❌ Parâmetros inválidos: {trade}" | ||
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| if amount > self.max_trade_amount and strategy not in ("STOP_LOSS", "TAKE_PROFIT"): | ||
| amount = self.max_trade_amount |
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Cash liberation sells truncated by USDC cap
Medium Severity
CASH_LIBERATION exit trades set amount to the position's share count, but the max-amount guard on line 642 only exempts STOP_LOSS and TAKE_PROFIT. This means a cash liberation sell of, say, 50 shares gets capped at max_trade_amount (a USDC value, e.g. 25), truncating the sell. This is a unit mismatch (shares vs USDC) and defeats the purpose of selling a full position to free up capital.
Additional Locations (1)
Major upgrades to professional trading bot level: - Add PriceFeed (WebSocket + parallel HTTP) for real-time Binance prices (~100ms latency vs 7s sequential polling) - Add dedicated ARB loop (1-3s cycles) for latency arbitrage on 15-min crypto markets (BTC/ETH/SOL/DOGE/XRP/AVAX/LINK) - Add WhaleTracker to monitor top Polymarket traders via leaderboard API, detect new positions, size increases, exits, and whale consensus signals - Add multi-timeframe analysis (1m, 5m, 15m, 1h) from Binance klines - Add few-shot learning: LLM receives recent winning trades as examples - Add whale signals to LLM context for smarter trade decisions - New Telegram commands: /dashboard, /whales, /arb - Strategy engine accepts external prices (crypto_prices_override) to skip HTTP calls when PriceFeed is active - Auto-restart /autotrade_live if already running (stop + start) - TradeDB: add get_recent_winning_trades() for few-shot learning Architecture: ARB(1-3s) + FAST(30s) + DEEP(5min) triple-speed loop Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
- Add ssl context with CERT_NONE for Binance WebSocket (fixes CERTIFICATE_VERIFY_FAILED on some systems) - Add 3 default whale wallets from Polymarket leaderboard analysis (addresses with $1M+ PnL confirmed) - WhaleTracker now starts with default wallets instead of requiring leaderboard API (which is not publicly accessible) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Cursor Bugbot has reviewed your changes and found 4 potential issues.
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| for tid in token_ids: | ||
| if tid in self._dead_markets: | ||
| continue |
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Dead-market check loop does not skip cache insertion
Medium Severity
The for tid in token_ids loop with continue only skips to the next iteration of the inner loop — it never prevents cache[token_ids[0]] = ... from executing. Dead/expired markets are always added to the arb cache regardless, causing the ARB loop to repeatedly attempt trades on resolved markets that will fail.
| token_limit = max_token_model.get(model, 15000) | ||
| return ChatOpenAI(model=model, temperature=0), token_limit | ||
| else: | ||
| raise ValueError("No LLM API key. Set XAI_API_KEY or OPENAI_API_KEY in .env") |
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Executor missing Anthropic provider unlike other LLM builders
Medium Severity
_build_executor_llm only supports xAI and OpenAI, while _build_llm in both agent.py and auto_trader.py prioritizes Anthropic first. A user who sets only ANTHROPIC_API_KEY (following the documented priority) will get a ValueError from the Executor, causing it to silently fail during agent initialization.
| # Create SSL context that doesn't verify certs (Binance WS sometimes has chain issues) | ||
| self._ssl_ctx = _ssl.create_default_context() | ||
| self._ssl_ctx.check_hostname = False | ||
| self._ssl_ctx.verify_mode = _ssl.CERT_NONE |
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SSL certificate verification disabled for price feed WebSocket
Medium Severity
SSL certificate verification is completely disabled (check_hostname = False, verify_mode = CERT_NONE) for the Binance WebSocket connection. This makes the price feed vulnerable to MITM attacks. Since this data drives real-money trading decisions (especially the latency arbitrage loop), an attacker could inject fake prices to trigger manipulated trades.
| else: | ||
| crypto_prices = self.crypto_monitor.get_crypto_prices() | ||
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| regime = self.crypto_monitor.detect_market_regime() |
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Regime detection always returns "unknown" with PriceFeed active
Medium Severity
When crypto_prices_override is provided (the primary path when PriceFeed is active), the code updates price_cache and _last_fetch but never appends to price_history. Since detect_market_regime on line 611 checks len(self.price_history) < 10 and returns "unknown" when there's insufficient data, regime detection is permanently broken on the intended primary path. The regime-based confidence boosts for volatile/ranging markets are never applied despite the comment on line 605 claiming "regime detection works."
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Summary
scripts/python/telegram_bot.py): Full-featured Telegram interface for the Polymarket trading agent with auto-execute smart bets, confirmation buttons for buy/sell, goal-based auto-trading, and real-time notificationsscripts/python/auto_trader.py+scripts/python/strategies.py): Autonomous trading engine based on analysis of top Polymarket bot strategies, including:scripts/python/agent.py): Multi-LLM provider support (Claude > xAI Grok > OpenAI), smart bet auto-execution, improved market search with local keyword filteringKey Features
Test plan
python scripts/python/telegram_bot.py/auto startcommand🤖 Generated with Claude Code
Note
High Risk
Introduces automated trade execution and remote control via Telegram, and changes wallet/CLOB order placement behavior (including aggressive orders and proxy wallet support), which can directly impact funds if misconfigured or buggy.
Overview
Adds a new conversational Polymarket agent runnable via CLI and Telegram, including wallet-aware read-only vs trading modes, command parsing via LLM, and interactive order confirmation/auto-goal flows.
Introduces a large autonomous auto-trader with real-time Binance price ingestion (WebSocket + HTTP fallback), strategy scanning (latency arb, parity arb, NO-bias, high-prob compounding, longshots, plus portfolio/exit management), and direct execution paths with dry-run support.
Hardens and extends core integrations: multi-provider LLM selection (xAI/OpenAI) in
Executor, new Gamma market keyword search and request timeouts, and Polymarket CLOB updates (web3 v7 middleware compatibility, new Polygon RPC, proxy-wallet support, aggressive/market-style order helpers, balances/positions/orders APIs). Also updates.env.exampleand addspython-telegram-botdependency plus CLI entrypoints foragent/telegram.Written by Cursor Bugbot for commit 9e9ae6a. This will update automatically on new commits. Configure here.