Dynamic Grid Trading Strategy v1.0

Cron-driven grid bot for EVM L2 chains via onchainos CLI. Features asymmetric grid steps — different spacing for buy vs sell sides based on trend direction, trend intelligence with multi-timeframe analysis, sell optimization, and HODL Alpha tracking.

Every tick: fetch price → MTF analysis → compute grid level → trend-adaptive decision → execute swap → report to Discord.

Asymmetric Grid Steps

Buy and sell sides use different spacing based on trend direction:

Key config: ASYM_FACTOR=0.4 (max asymmetry ratio). Asymmetry scales with trend strength and only activates when strength > 0.3.

New grid dict fields: buy_step, sell_step (backward-compatible step = average). Level prices are now non-uniform: below center spaced by buy_step, above center by sell_step.

Architecture

Cron (5min) → Python script → onchainos CLI → OKX Web3 API → Chain
                  ↓                ↓
            grid_state.json    Wallet (TEE signing)
                  ↓
            ┌─────────────┐
            │ MTF Analysis │ ← price_history (288 bars = 24h)
            │ K-line ATR   │ ← okx-dex-market kline (1H × 24)
            └──────┬──────┘
                   ↓
            Trend-Adaptive Grid Decision
                   ↓
            Discord embed (notification)

OKX Skill Dependencies (command syntax defined in each skill, do not duplicate here):

• okx-dex-swap — quote, approve, swap execution
• okx-dex-market — K-line / OHLC data
• okx-agentic-wallet — wallet switch, balance, contract-call (TEE signing)
• okx-onchain-gateway — transaction simulation

Pipeline: Execution Steps

CRITICAL RULE: Steps MUST execute in order. Do NOT skip steps or proceed past a gate that has not been satisfied.

Step 1: Data Acquisition

Actions:

1. Fetch ETH price via okx-dex-swap (swap quote)
2. Fetch on-chain balances via okx-agentic-wallet (wallet balance)
3. Update price_history (append, cap at 288 = 24h @ 5min)
4. Detect external deposits/withdrawals (unexplained balance changes > $100)

Gate (ALL must pass):

• [ ] Price is non-null and > 0
• [ ] At least one balance (ETH or USDC) > 0
• [ ] Circuit breaker not active (consecutive_errors < 5)
• [ ] Stop not triggered (stop_triggered == null)

Step 2: Multi-Timeframe Analysis

Actions:

1. Compute short EMA (25min / 5-bar), medium EMA (1h / 12-bar), long EMA (4h / 48-bar)
2. Detect EMA alignment → trend direction (bullish / bearish / neutral) + strength (0-1)
3. Detect 8h structure: split into 4 segments, check higher-highs/higher-lows → uptrend / downtrend / ranging
4. Compute 1h and 4h momentum
5. Fetch K-line data (1H candles, 24 bars) → compute ATR-based volatility (hourly cache)

Output: mtf dict, kline_vol float

def analyze_multi_timeframe(history, price) -> dict:
    # Returns {trend, strength, momentum_1h, momentum_4h, structure,
    #          ema_short, ema_medium, ema_long}
    # EMA alignment: short > medium > long → bullish
    # Structure: 8h window split into 4 segments
    #   higher-highs + higher-lows → "uptrend"
    #   lower-highs + lower-lows → "downtrend"
    #   else → "ranging"

Gate:

• [ ] mtf dict has trend and strength fields (graceful fallback to neutral if insufficient history)

Step 3: Grid Decision

Actions:

1. Calculate dynamic grid with trend-adaptive volatility multiplier:
• Grid center = EMA(20) on 1H kline (20-hour EMA, fetched via okx-dex-market kline). Falls back to 5min tick history if kline unavailable.
• Base: VOLATILITY_MULTIPLIER_BASE=2.0
• In trend (strength > 0.3): blend toward VOLATILITY_MULTIPLIER_TREND=3.0
• Wider grid in trends → fewer trades → more holding
2. Check recalibration triggers (breakout / vol shift / age)
3. Map price → grid level
4. If level changed: determine direction (BUY if level dropped, SELL if rose)
5. Safety checks: cooldown, trend-adaptive position limits, repeat guard, consecutive limit
6. Sell optimization: if SELL in strong uptrend, delay via _should_delay_sell()
7. Calculate trade size with trend-adaptive sizing

Gate:

• [ ] Grid is valid (step > 0, levels > 0)
• [ ] If trade needed: amount >= MIN_TRADE_USD ($5)
• [ ] All safety checks passed (cooldown, position limits, etc.)

Step 4: Execution

Actions:

1. Get swap quote + tx data from OKX DEX aggregator
2. Pre-simulate via okx-onchain-gateway (diagnostic, non-blocking)
3. For BUY: ensure USDC approval via okx-dex-swap
4. Sign + broadcast via okx-agentic-wallet contract-call (TEE signing)
5. On failure: 1 auto-retry with 3s delay and fresh quote
6. Record trade in state, update grid level ONLY on success

Gate:

• [ ] Transaction hash received, or failure recorded with retriable flag
• [ ] Level updated only on success; NOT on failure or skip

Step 5: Notification & Tracking

Actions:

1. Calculate PnL (USD + ETH denominated)
2. Calculate HODL Alpha: current_portfolio - (initial_eth × current_price)
3. Build structured JSON output for AI agent parsing
4. Send Discord embed (green=SELL, blue=BUY, grey=no-trade, red=stop)
5. Emit ---JSON--- block with enriched fields

Output: Discord notification + structured JSON

Tunable Parameters

Grid Structure

Multi-Timeframe

Sell Optimization

Grid Modes

Arithmetic (等差网格): Each level is a fixed USD distance apart. Good for narrow ranges.

levels = [center - N*step, ..., center, ..., center + N*step]

Geometric (等比网格): Each level is a fixed percentage apart. Better for wide ranges because step size scales with price. The ratio is derived from the arithmetic step: ratio = 1 + (step / center).

# In calc_dynamic_grid(), when GRID_TYPE == "geometric":
ratio = 1 + (step / center)
level_prices = [center * (ratio ** (i - half)) for i in range(GRID_LEVELS + 1)]

Both modes store level_prices in the grid dict for unified level lookup via bisect_right. Asymmetric spacing uses levels below center spaced by buy_step, levels above by sell_step. The _build_level_prices() helper handles both symmetric and asymmetric construction.

Choosing a mode:

Adaptive Step Sizing

Step scales with real-time volatility, modulated by trend strength. Splits into directional buy/sell steps:

vol_mult = VOLATILITY_MULTIPLIER_BASE  (2.0)
if trend_strength > 0.3:
    vol_mult = blend(BASE, TREND, strength)  (up to 3.0)

# Asymmetric steps based on trend direction
asym = ASYM_FACTOR × strength  (if strength > 0.3, else 0)
if bullish:
    buy_mult  = vol_mult × (1 - asym)   # tighter buy
    sell_mult = vol_mult × (1 + asym)   # wider sell
elif bearish:
    buy_mult  = vol_mult × (1 + asym)   # wider buy
    sell_mult = vol_mult × (1 - asym)   # tighter sell

buy_step  = (buy_mult × ATR) / (GRID_LEVELS / 2)
sell_step = (sell_mult × ATR) / (GRID_LEVELS / 2)
# Both clamped to [price × STEP_MIN_PCT, price × STEP_MAX_PCT], floor $5
step = (buy_step + sell_step) / 2  # backward-compatible average

Recalibration triggers (asymmetric):

1. Downside breakout: Price < grid lower - buy_step → recalibrate immediately (buying dips is grid's edge)
2. Upside breakout: Price > grid upper + sell_step → require N consecutive ticks confirmation before recalibrating (anti-chase)
3. Grid age exceeds GRID_RECALIBRATE_HOURS
4. Current kline ATR deviates >30% from grid's stored ATR

Anti-chase mechanism:

• Upside breakout counter resets if price returns to grid range before confirmation
• Even after confirmation, center shifts are capped to MAX_CENTER_SHIFT_PCT per recalibration
• Multiple recalibrations can gradually track a true trend, but a single spike cannot drag the grid

Position Sizing Strategies

Controls how much to trade at each grid level.

def _calc_sizing_multiplier(level, grid_levels, direction, mtf=None):
    base_mult = 1.0
    if SIZING_STRATEGY == "trend_adaptive" and mtf:
        trend = mtf.get("trend", "neutral")
        strength = mtf.get("strength", 0)
        if trend == "bullish":
            if direction == "BUY":
                base_mult = 1.0 + strength * (MAX - 1.0)   # buy aggressively
            else:
                base_mult = 1.0 - strength * (1.0 - MIN)   # sell conservatively
        elif trend == "bearish":
            # opposite
            ...
    return clamp(base_mult, SIZING_MULTIPLIER_MIN, SIZING_MULTIPLIER_MAX)

Trade Execution

Risk Controls

Basic Controls

Additional safety guards:

• Rapid drop protection: skip BUY if price dropped >2% in last 30min (6 ticks)
• Consecutive same-direction reset: limit resets if grid was recalibrated or >1h since last trade
• Anti-repeat: skip if same direction + same level boundary as last trade
• Trend-adaptive position limits: limits shift based on trend direction and strength

Stop-Loss, Trailing Stop & Take-Profit

Three protection mechanisms. When triggered, trading halts and a red Discord alert is sent. Use resume-trading command to clear.

def _check_stop_conditions(state, total_usd, price):
    cost_basis = initial + deposits
    pnl_pct = (total_usd - cost_basis) / cost_basis
    peak = max(stats["portfolio_peak_usd"], total_usd)
    # Check: stop_loss, trailing_stop, take_profit

Risk Control Flow in tick()

1. If stop_triggered is set → log + Discord red alert + refuse trading + return
2. Check _check_stop_conditions → if triggered, set stop_triggered + alert + return
3. Multi-timeframe analysis → get trend context
4. Normal grid logic (cooldown, trend-adaptive position limits, etc.)
5. If SELL in strong uptrend → _should_delay_sell() check

Core Algorithm

1. Fetch token price
2. Read on-chain balances (ETH + USDC)
3. Multi-timeframe analysis → trend/strength/momentum/structure
4. Fetch K-line ATR volatility (hourly cache)
5. Check if grid needs recalibration (breakout / vol shift / age)
   → calc_dynamic_grid() uses trend-adaptive volatility multiplier
   → asymmetric buy_step/sell_step based on trend direction
6. Map price → grid level
7. If level changed:
   a. Direction: BUY if level dropped, SELL if rose
   b. If SELL in strong uptrend → delay check (trailing + momentum protection)
   c. Safety checks (cooldown, trend-adaptive position limits, repeat guard, consecutive limit)
   d. Calculate trade size (trend-adaptive sizing)
   e. Execute swap via DEX aggregator
   f. Record trade, update level ONLY on success
8. Calculate HODL Alpha
9. Report status (JSON + Discord)

Grid Calculation

def calc_dynamic_grid(price, price_history, mtf=None):
    center = EMA(1H_kline, EMA_PERIOD)  # 20-hour EMA on 1H candles
    atr = calc_kline_volatility(candles)

    # Trend-adaptive multiplier
    vol_mult = VOLATILITY_MULTIPLIER_BASE  # 2.0
    if mtf and mtf["strength"] > 0.3:
        vol_mult = blend(BASE=2.0, TREND=3.0, factor=strength)

    # Asymmetric buy/sell multipliers
    asym = ASYM_FACTOR * strength if strength > 0.3 else 0
    if bullish:
        buy_mult = vol_mult * (1 - asym)   # tighter buy grid
        sell_mult = vol_mult * (1 + asym)   # wider sell grid
    elif bearish:
        buy_mult = vol_mult * (1 + asym)   # wider buy grid
        sell_mult = vol_mult * (1 - asym)   # tighter sell grid

    buy_step = clamp((buy_mult * atr) / half, floor, ceil)
    sell_step = clamp((sell_mult * atr) / half, floor, ceil)

    # Build asymmetric level_prices via _build_level_prices()
    # Below center: spaced by buy_step; Above center: spaced by sell_step
    level_prices = _build_level_prices(center, buy_step, sell_step, half, grid_type)

    return {center, step, buy_step, sell_step, levels, range, vol_pct, type, level_prices}

Examples (at price $2000, ATR=$50):

Sell Optimization Logic

def _should_delay_sell(state, current_level, prev_level, mtf, history):
    """Returns skip reason or None."""

    # 1. Momentum protection: skip sell if 1h momentum > 0.5% in uptrend
    if momentum_1h > SELL_MOMENTUM_THRESHOLD * 100:
        if trend == "bullish" and structure == "uptrend":
            return "trend_hold (momentum +X.X%)"

    # 2. Trailing sell: wait SELL_TRAIL_TICKS (2) before executing
    trail = state["sell_trail_counter"]
    level_key = f"{prev_level}->{current_level}"
    if trail[level_key] < SELL_TRAIL_TICKS:
        trail[level_key] += 1
        return "sell_trail (N/2)"

    # 3. Cleared → proceed with sell
    return None

Trend-Adaptive Position Limits

def _get_position_limits(mtf):
    """Return (max_pct, min_pct) based on trend."""
    if trend == "bullish" and strength > 0.3:
        max_pct = 70 + (80 - 70) * strength  # allow more ETH
        min_pct = 30
    elif trend == "bearish" and strength > 0.3:
        max_pct = 70
        min_pct = 30 - (30 - 25) * strength  # allow less ETH
    else:
        max_pct, min_pct = 70, 30

Trade Size

Returns (amount_in_smallest_unit, failure_info). SELL returns wei (x1e18), BUY returns uUSDC (x1e6).

def calc_trade_amount(direction, eth_bal, usdc_bal, price,
                      current_level=None, grid_levels=None,
                      mtf=None):
    available_eth = eth_bal - GAS_RESERVE_ETH
    total_usd = available_eth * price + usdc_bal
    max_usd = total_usd * MAX_TRADE_PCT

    # Apply trend-adaptive sizing
    multiplier = _calc_sizing_multiplier(level, grid_levels, direction, mtf)
    max_usd *= multiplier

    if direction == "SELL":
        return int(min(max_usd / price, available_eth) * 1e18), None  # wei
    else:
        return int(min(max_usd, usdc_bal * 0.95) * 1e6), None  # uUSDC

Level Update Rule (Critical)

PnL Tracking (dual-denominated)

# USD-denominated
total_pnl_usd = current_portfolio_usd - cost_basis
hodl_value_usd = initial_eth_amount × current_price
grid_alpha_usd = current_portfolio_usd - hodl_value_usd

# ETH-denominated
current_eth_equivalent = current_portfolio_usd / current_price
initial_eth_equivalent = cost_basis / initial_price
total_pnl_eth = current_eth_equivalent - initial_eth_equivalent

HODL Alpha (key metric): grid_alpha_usd > 0 means grid outperforms pure ETH holding.

State Schema

{
  "version": 5,
  "grid": {"center": 2000, "step": 43.5, "buy_step": 33.3, "sell_step": 53.7,
           "levels": 6, "range": [1900, 2161], "vol_pct": 2.1,
           "type": "arithmetic",
           "level_prices": [1900, 1933, 1967, 2000, 2054, 2107, 2161]},
  "grid_set_at": "ISO timestamp",
  "current_level": 3,
  "price_history": ["...max 288 (24h at 5min)"],
  "trades": [{"time": "...", "direction": "SELL", "price": 2050,
              "amount_usd": 25, "est_profit": 1.5, "tx": "0x...",
              "grid_from": 2, "grid_to": 3}],
  "stats": {
    "total_trades": 15,
    "realized_pnl": 5.2,
    "grid_profit": 3.8,
    "initial_portfolio_usd": 1000,
    "initial_eth_price": 2000.0,
    "portfolio_peak_usd": 1050.0,
    "total_deposits_usd": 0.0,
    "deposit_history": [],
    "trade_attempts": 10, "trade_successes": 10, "trade_failures": 0,
    "sell_attempts": 5, "sell_successes": 5,
    "buy_attempts": 5, "buy_successes": 5,
    "retry_attempts": 0, "retry_successes": 0,
    "started_at": "ISO timestamp",
    "last_check": "ISO timestamp"
  },
  "stop_triggered": null,
  "last_trade_times": {"BUY": "...", "SELL": "..."},
  "last_failed_trade": null,
  "last_balances": {"eth": 0.134, "usdc": 257.33, "time": "ISO timestamp"},
  "last_quiet_report": "ISO timestamp",
  "upside_breakout_ticks": 0,
  "approved_routers": ["0x..."],
  "errors": {"consecutive": 0, "cooldown_until": null},
  "mtf_cache": null,
  "kline_cache": null,
  "sell_trail_counter": {}
}

Key fields:

• grid.type + grid.level_prices: geometric/arithmetic grid support (asymmetric spacing)
• grid.buy_step / grid.sell_step: directional step sizes; grid.step = average for backward compat
• stats.initial_eth_price: records ETH price at bot start for HODL Alpha calculation
• stats.portfolio_peak_usd: highest portfolio value (for trailing stop)
• stop_triggered: string describing trigger condition, or null
• last_failed_trade: cached for retry command (expires after 10min)
• upside_breakout_ticks: confirmation counter for upside recalibration
• approved_routers: USDC approval cache to avoid redundant approvals
• mtf_cache: cached multi-timeframe analysis result
• kline_cache: cached K-line data (1h TTL)
• sell_trail_counter: tracks sell delay tick counts per level transition

Operational Interface

Sub-Commands

COMMANDS = {
    "tick": tick, "status": status, "report": report,
    "history": history_cmd, "reset": reset, "retry": retry,
    "analyze": analyze, "deposit": deposit,
    "resume-trading": resume_trading
}
if __name__ == "__main__":
    cmd = sys.argv[1] if len(sys.argv) > 1 else "tick"
    COMMANDS.get(cmd, tick)()

AI Agent Output Protocol

The tick command outputs a structured JSON block for AI agent parsing:

---JSON---
{
  "version": "1.0",
  "status": "trade_executed" | "no_trade" | "cooldown" | "trade_failed" | ...,
  "market": {
    "price": 2090.45, "ema": 2085.3, "volatility_pct": 1.2,
    "trend": "bullish", "trend_strength": 0.65,
    "momentum_1h": 0.35, "momentum_4h": 1.2,
    "structure": "uptrend",
    "kline_atr_pct": 1.8
  },
  "portfolio": {"eth": 0.134, "usdc": 257.33, "total_usd": 538.0, "eth_pct": 52.1},
  "grid_level": 3,
  "direction": "SELL",
  "tx_hash": "0x...",
  "failure_reason": "...",
  "retriable": true,
  "hodl_alpha": 2.15,
  "success_rate": {"total_attempts": 182, "successes": 182, "rate_pct": 100.0}
}

The analyze command outputs additional fields:

• multi_timeframe: full MTF data (EMA short/medium/long, momentum, structure)
• round_trips: trade pair analysis (good / micro / loss classification)

Discord Notification

Two card formats pushed via Discord Bot API:

Trade executed (colored embed):

• Green = SELL, Blue = BUY
• Fields: price, level, total value, position, PnL, grid profit, HODL Alpha, BaseScan link

No trade (grey compact card):

• One-line: price . level . position . PnL . trend . trade count
• Only sent once per QUIET_INTERVAL (default 1 hour)

Deposit/Withdrawal Detection

Automatically detects external balance changes:

unexplained_change = (current_balance - last_balance) - sum(recorded_trades_since_last)
if abs(unexplained_change) > $100:
    record as deposit or withdrawal → adjust PnL cost basis

Logging

• File: grid_bot.log in script directory
• Rotation: simple half-file rotation at 1MB
• Format: [YYYY-MM-DD HH:MM:SS] message

Adapting to Different Pairs

AI Review & Optimization

AI agent should periodically review trading performance and suggest/apply optimizations. Run weekly or when cumulative PnL stalls.

Step 1: Pull & Pair Trades

Extract recent trades and pair each BUY with its corresponding SELL to form round trips.

# Matching logic: SELL from level A→B matches BUY from level B→A
buy_stack = []
round_trips = []
for trade in trades:
    if trade["direction"] == "BUY":
        buy_stack.append(trade)
    else:  # SELL
        for j in range(len(buy_stack)-1, -1, -1):
            if buy_stack[j]["grid_to"] == trade["grid_from"]:
                matched_buy = buy_stack.pop(j)
                round_trips.append((matched_buy, trade))
                break

Output per round trip:

• Spread: (sell_price - buy_price) / buy_price x 100%
• Hold time: minutes between buy and sell
• Status: profit (spread > 0.3%), micro-profit (0 < spread < 0.3%), loss (spread < 0)

Step 2: Flag Anomalies

Key metrics: win rate, loss impact, micro-trade ratio (if > 30%, step too small).

Step 3: Root Cause Analysis

LOSS trades:

MICRO trades:

Step 4: Parameter Tuning

STEP_MIN_PCT >= DEX_total_cost x 3
  where DEX_total_cost ~ slippage + price_impact ~ 0.1-0.3% on L2
  → STEP_MIN_PCT >= 0.009 to 0.012

UPSIDE_CONFIRM_TICKS = typical_spike_duration / tick_interval
  e.g., spikes last ~20min, tick=5min → confirm_ticks = 4-6

MAX_CENTER_SHIFT_PCT = step_pct x 2-3

Step 5: Backtest & Apply

Simulate new parameters against historical data, then: backup → patch → recalibrate → monitor 24h → re-run analysis.

Review Checklist (AI Agent Prompt)

1. Read grid_state.json and grid_bot.log
2. Filter trades to review window (default: last 48h)
3. Pair trades into round trips
4. Compute: win_rate, avg_spread, loss_count, micro_count, total_pnl, hodl_alpha
5. If loss_count > 0: trace each loss to recalibration events
6. If micro_ratio > 30%: recommend STEP_MIN_PCT increase
7. Check MTF data for trend alignment during losses
8. Propose specific parameter changes with backtest evidence
9. On user approval: backup → patch → recalibrate → verify

Failure & Rollback

IF Step N fails:
  1. Log failure reason to grid_bot.log
  2. Increment errors.consecutive
  3. If errors.consecutive >= 5: trigger circuit breaker (1h cooldown)
  4. Cache failed trade for retry command (10min expiry)
  5. DO NOT update grid level
  6. Report failure via Discord + JSON output

Anti-Patterns