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Binance and GPts APIs but cannot connect #587

@xiaosma

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@xiaosma

Note: set AGENT_I am using Binance and GPT API but cannot connect. I am in China and have used a proxy. The logs also show: RedrawEventsCleared emitted without explicit MainEventsCleared [2026-01-04][14:59:42][tao::platform_impl::platform::event_loop::runner][WARN] NewEvents emitted without explicit RedrawEventsCleared [2026-01-04][14:59:42][tao::platform_impl::platform::event_loop::runner][WARN] RedrawEventsCleared emitted without explicit MainEventsCleared [2026-01-04][15:30:39][tao::platform_impl::platform::event_loop::runner][WARN] NewEvents emitted without explicit RedrawEventsCleared [2026-01-04][15:30:39][tao::platform_impl::platform::event_loop::runner][WARN] RedrawEventsCleared emitted without explicit MainEventsCleared [2026-01-04][15:30:57][tao::platform_impl::platform::event_loop::runner][WARN] NewEvents emitted without explicit RedrawEventsCleared [2026-01-04][15:30:57][tao::platform_impl::platform::event_loop::runner][WARN] RedrawEventsCleared emitted without explicit MainEventsCleared [2026-01-04][15:31:42][tao::platform_impl::platform::event_loop::runner][WARN] NewEvents emitted without explicit RedrawEventsCleared [2026-01-04][15:31:42][tao::platform_impl::platform::event_loop::runner][WARN] RedrawEventsCleared emitted without explicit MainEventsClearedDEBUG_MODE=true to trace model behavior locally.

and


backend.log

Start your response with `{` and end it with `}`.                        
  Your output will be passed to json.loads() to convert it to a Python     
  object.                                                                  
  Make sure it only contains valid JSON.                                   

DEBUG =========================== user ===========================
DEBUG Read Context and decide. features.1m = structural trends (240 periods),
features.1s = realtime signals (180 periods). market.funding_rate:
positive = longs pay shorts. Respect constraints and risk_flags. Prefer
NOOP when edge unclear. Always include a concise top-level 'rationale'.
If you choose NOOP (items is empty), set 'rationale' to explain why:
reference current prices and 'price.change_pct' vs thresholds, and any
constraints or risk flags that led to NOOP. Output JSON with items array.

  Context:                                                                 
  {"strategy_prompt": "Goal\nProduce steady, risk‑aware crypto trading     
  decisions that aim for consistent small gains while protecting           
  capital.\n\nStyle & constraints\n- Focus on liquid majors (e.g., BTC-USD,
  ETH-USD). Avoid low-liquidity altcoins.\n- Size conservatively: target at
  most 1–2% of NAV per new trade (respect cap_factor).\n- Limit concurrent 
  open positions (respect max_positions from constraints/config).\n- Prefer
  market or tight-limit entries on pullbacks; avoid chasing large, fast    
  moves.\n- Favor trend‑aligned entries; if the mid‑term trend is unclear, 
  sit out.\n- Avoid entries during maintenance windows or very low volume  
  periods.\n\nSignals & decision heuristics\n- Trend detection: short EMA  
  (e.g., 20) vs long EMA (e.g., 100); bias with the trend.\n- Momentum     
  confirmation: avoid overbought entries; prefer RSI turning up from       
  balanced/oversold zones.\n- Volatility filter: when realized volatility  
  is high, reduce size or skip.\n- Pullbacks: enter nearer to moving       
  averages or support zones; reduce chasing breakouts.\n- Confluence:      
  require at least two agreeing signals (trend +                           
  momentum/volume/structure).\n\nOrder sizing & execution\n- Notional =    
  min(cap_factor * equity, available buying power, venue caps).\n- Convert 
  notional → quantity using mark price; clamp to min_trade_qty,            
  max_order_qty, and quantity_step.\n- Use market orders for small         
  rebalances; prefer tight-limit for larger orders to control slippage.\n- 
  If partial fills occur, retry briefly; then treat as partial and update  
  portfolio.\n\nRisk management (mandatory)\n- Stops: place below recent   
  support/ATR multiples for longs; above resistance for shorts.\n- Targets:
  risk:reward at least 1:1.5 by default (configurable).\n- Trailing:       
  consider converting to trailing after ~1x risk achieved.\n- Portfolio    
  risk cap: aggregated potential loss should not exceed a fraction of      
  NAV.\n- Fees: include estimated fees when sizing and evaluating          
  P/L.\n\nPosition management & lifecycle\n- On open: record entry price,  
  notional, leverage, and planned stop/target in trade meta.\n- Re-evaluate
  each cycle: close on stop/target hit or regime flip; avoid frequent      
  flip-flopping.\n- Flip-by-flat: fully close before reversing direction on
  the same symbol.\n- cautious full deployment\n    - Purpose: staged      
  scale‑in for high‑conviction, well‑liquid opportunities.\n    - Entry    
  criteria: high digest/win_rate + strong multi‑factor confluence;         
  sufficient buying_power; no blocking risk_flags.\n    - Execution: scale 
  in 2–3 tranches (e.g., 50/30/20); require confirmation between tranches; 
  start with lower leverage.\n    - Safety: ensure liquidation distance >  
  configured margin; abort remaining tranches on large slippage or failed  
  fills.\n    - Post‑deploy: enable trailing stops and de‑risk if          
  risk_flags flip.\n\nEdge cases & guards\n- If computed quantity <        
  min_trade_qty, skip.\n- If spread/slippage estimate is large, skip or    
  reduce size.\n- If data is stale (last candle older than 2× interval),   
  skip for that symbol.\n\nRationale and explainability\n- For each action,
  include a short rationale: which signals agreed and the stop/target      
  idea.\n- For skipped signals, include a brief reason (e.g., volatility   
  too high, below min_notional).\n\nTelemetry & meta\n- Attach: compose_id,
  strategy_id, timestamp, estimated_fee, estimated_notional,               
  confidence_score.\n- Confidence: normalize to [0,1]; proportionally      
  reduce size when confidence is low (< 0.5).\n\nSummary (one sentence)\nBe
  conservative and trend‑aware: take small, well‑sized positions on        
  pullbacks or confirmed breakouts, protect capital with explicit stops,   
  and favor repeatable gains over risky bets.\n\n", "summary":             
  {"active_positions": 0, "total_value": 1000.0, "account_balance": 1000.0,
  "free_cash": 1000.0, "unrealized_pnl": 0.0}, "constraints":              
  {"max_positions": 5, "max_leverage": 2.0}}                               

INFO:httpx:HTTP Request: POST https://api.deepseek.com/v1/chat/completions "HTTP/1.1 402 Payment Required"
ERROR API status error from OpenAI API: Error code: 402 - {'error':
{'message': 'Insufficient Balance', 'type': 'unknown_error', 'param':
None, 'code': 'invalid_request_error'}}
2026-01-04 23:53:22.795 | ERROR | valuecell.agents.common.trading.decision.prompt_based.composer:compose:102 - LLM invocation failed: Insufficient Balance
2026-01-04 23:53:22.795 | INFO | valuecell.agents.common.trading._internal.coordinator:run_once:182 - \U0001f50d Composer returned 0 instructions
2026-01-04 23:53:22.795 | INFO | valuecell.agents.common.trading._internal.coordinator:run_once:189 - \U0001f680 Calling execution_gateway.execute() with 0 instructions
2026-01-04 23:53:22.795 | INFO | valuecell.agents.common.trading._internal.coordinator:run_once:192 - ExecutionGateway type: PaperExecutionGateway
2026-01-04 23:53:22.795 | INFO | valuecell.agents.common.trading._internal.coordinator:run_once:198 - \u2705 ExecutionGateway returned 0 results
2026-01-04 23:53:22.796 | INFO | valuecell.agents.common.trading.base_agent:_run_background_decision:261 - Run cycle completed for strategy=strategy-27a6a01d521243c1981d5f7e443825d5 trades_count=0
2026-01-04 23:53:22.812 | INFO | valuecell.agents.common.trading._internal.stream_controller:persist_cycle_results:270 - Persisted portfolio view for strategy=strategy-27a6a01d521243c1981d5f7e443825d5
2026-01-04 23:53:22.820 | INFO | valuecell.agents.common.trading._internal.stream_controller:persist_cycle_results:276 - Persisted strategy summary for strategy=strategy-27a6a01d521243c1981d5f7e443825d5
2026-01-04 23:53:22.820 | INFO | valuecell.agents.common.trading.base_agent:_run_background_decision:278 - Waiting for next decision cycle for strategy_id=strategy-27a6a01d521243c1981d5f7e443825d5, interval=12seconds
INFO: ::1:54301 - "GET /api/v1/strategies/holding?id=strategy-27a6a01d521243c1981d5f7e443825d5 HTTP/1.1" 200 OK
INFO: ::1:54301 - "GET /api/v1/strategies/portfolio_summary?id=strategy-27a6a01d521243c1981d5f7e443825d5 HTTP/1.1" 200 OK
INFO: ::1:54301 - "GET /api/v1/strategies/detail?id=strategy-27a6a01d521243c1981d5f7e443825d5 HTTP/1.1" 200 OK
INFO: ::1:54301 - "GET /api/v1/strategies/holding_price_curve?id=strategy-27a6a01d521243c1981d5f7e443825d5 HTTP/1.1" 200 OK
INFO: ::1:54301 - "GET /api/v1/strategies/ HTTP/1.1" 200 OK
INFO: ::1:54301 - "GET /api/v1/healthz HTTP/1.1" 200 OK

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