Arbiter Bot - For All Hive Engine Tokens

I've received constant feedback, people aren't interested in the games.

They want to make money here on Hive.

They want their tokens to make money here on Hive.

Well, here's the beginning if you feed the bot, it will raise the prices.

There will be several other files. I'll try to keep them all linked together, so if you read the next one you will at least find this one.

import os
import json
import subprocess
import time
from datetime import datetime
from beem import Hive

from fetch_market import get_orderbook_top
from place_order import get_balance, place_order, get_open_orders, safe_float


DEFAULT_CONFIG = {
    "account_name": "YOUR_HIVE_ACCOUNT",
    "active_key": "YOUR_ACTIVE_KEY",
    "token": "ARBITER",
    "quote_token": "SWAP.HIVE",
    "target_price": 0.10,
    "spread": 0.004,
    "sell_allocation_pct": 0.10,
    "buy_allocation_pct": 0.50,
    "min_quote_for_buy": 0.01,
    "min_profit_spread_pct": 0.004,
    "edge_buffer_pct": 0.001,
    "duplicate_price_tolerance_pct": 0.001,
    "min_rc_percent": 10,
    "low_rc_cycle_seconds": 1800,
    "initial_token_reserve": 1.0,
    "reserve_growth_per_cycle": 0.00000001,
    "cycle_seconds": 900,
    "cancel_every_n_cycles": 3,
    "hive_nodes": ["https://api.hive.blog", "https://anyx.io"],
}


def read_bot_info(path="bot_info.txt"):
    try:
        with open(path, "r", encoding="utf-8") as file:
            lines = [line.strip() for line in file if line.strip()]
            if len(lines) >= 2:
                return lines[0], lines[1]
    except Exception:
        pass
    return None, None


def run_cancel_once(script_path):
    try:
        subprocess.run(["python", script_path, "--once"], check=False)
    except Exception as exc:
        print(f"[WARN] Cancel helper failed: {exc}")


def safe_price(value):
    try:
        v = float(value)
        return v if v > 0 else 0.0
    except Exception:
        return 0.0


def clamp_01(value, default_value):
    try:
        v = float(value)
    except Exception:
        return default_value
    return min(max(v, 0.0), 1.0)


def safe_int(value, default_value, minimum=1):
    try:
        iv = int(value)
    except Exception:
        iv = default_value
    return max(iv, minimum)


def normalize_config(config):
    normalized = dict(config)

    normalized["target_price"] = safe_price(config.get("target_price"))
    normalized["spread"] = safe_price(config.get("spread"))
    normalized["min_quote_for_buy"] = safe_price(config.get("min_quote_for_buy"))
    normalized["min_profit_spread_pct"] = safe_price(config.get("min_profit_spread_pct"))
    normalized["edge_buffer_pct"] = safe_price(config.get("edge_buffer_pct"))
    normalized["duplicate_price_tolerance_pct"] = safe_price(config.get("duplicate_price_tolerance_pct"))
    normalized["min_rc_percent"] = safe_int(config.get("min_rc_percent"), 10, minimum=1)
    normalized["initial_token_reserve"] = safe_price(config.get("initial_token_reserve"))
    normalized["reserve_growth_per_cycle"] = safe_price(config.get("reserve_growth_per_cycle"))

    normalized["sell_allocation_pct"] = clamp_01(
        config.get("sell_allocation_pct"),
        DEFAULT_CONFIG["sell_allocation_pct"],
    )
    normalized["buy_allocation_pct"] = clamp_01(
        config.get("buy_allocation_pct"),
        DEFAULT_CONFIG["buy_allocation_pct"],
    )

    normalized["cycle_seconds"] = safe_int(
        config.get("cycle_seconds"),
        DEFAULT_CONFIG["cycle_seconds"],
        minimum=5,
    )
    normalized["cancel_every_n_cycles"] = safe_int(
        config.get("cancel_every_n_cycles"),
        DEFAULT_CONFIG["cancel_every_n_cycles"],
        minimum=1,
    )
    normalized["low_rc_cycle_seconds"] = safe_int(
        config.get("low_rc_cycle_seconds"),
        DEFAULT_CONFIG["low_rc_cycle_seconds"],
        minimum=5,
    )

    nodes = config.get("hive_nodes")
    if not isinstance(nodes, list) or not nodes:
        nodes = DEFAULT_CONFIG["hive_nodes"]
    normalized["hive_nodes"] = nodes

    return normalized


def setup_config_interactive(config_path):
    print("[SETUP] First-run setup for ARBITER bot")
    account_name = input("Hive account name: ").strip()
    active_key = input("Active key: ").strip()
    token = input("Token to market-make (default ARBITER): ").strip() or "ARBITER"

    target_price = 0.0
    while target_price <= 0:
        raw = input("Target price to maintain in SWAP.HIVE (e.g. 0.10): ").strip()
        target_price = safe_price(raw)
        if target_price <= 0:
            print("[SETUP] Target price must be greater than 0.")

    config = dict(DEFAULT_CONFIG)
    config.update(
        {
            "account_name": account_name,
            "active_key": active_key,
            "token": token,
            "target_price": target_price,
        }
    )

    with open(config_path, "w", encoding="utf-8") as file:
        json.dump(config, file, indent=2)
    print(f"[SETUP] Saved {config_path}")
    return config


def load_config(root):
    config_path = os.path.join(root, "bot_config.json")
    legacy_bot_info_path = os.path.join(root, "bot_info.txt")

    if os.path.exists(config_path):
        try:
            with open(config_path, "r", encoding="utf-8") as file:
                user_config = json.load(file)
            config = dict(DEFAULT_CONFIG)
            config.update(user_config if isinstance(user_config, dict) else {})
            return config
        except Exception as exc:
            print(f"[ERROR] Failed to read bot_config.json: {exc}")
            raise SystemExit(1)

    account, active_key = read_bot_info(legacy_bot_info_path)
    if account and active_key:
        config = dict(DEFAULT_CONFIG)
        config.update({"account_name": account, "active_key": active_key})
        with open(config_path, "w", encoding="utf-8") as file:
            json.dump(config, file, indent=2)
        print("[MIGRATE] Converted bot_info.txt to bot_config.json")
        return config

    return setup_config_interactive(config_path)


def build_prices(target_price, spread, highest_bid, lowest_ask):
    half_spread = max(spread / 2.0, 0.00000001)

    buy_price = max(target_price - half_spread, 0.00000001)
    sell_price = max(target_price + half_spread, buy_price + 0.00000001)

    # Keep the orders near current book edges while staying around the target.
    if highest_bid > 0:
        buy_price = min(buy_price, highest_bid + half_spread)
    if lowest_ask > 0:
        sell_price = max(sell_price, lowest_ask - half_spread)

    if sell_price <= buy_price:
        sell_price = buy_price + 0.00000001

    return buy_price, sell_price


def build_guarded_prices(
    target_price,
    spread,
    highest_bid,
    lowest_ask,
    edge_buffer_pct,
    min_profit_spread_pct,
):
    if highest_bid <= 0 or lowest_ask <= 0:
        return None, None

    live_spread = max(lowest_ask - highest_bid, 0.0)
    min_target_spread = max(spread, min_profit_spread_pct, 0.00000002)

    # If the book is inverted or too tight, widen around mid to keep margin.
    if live_spread < min_target_spread:
        mid = (highest_bid + lowest_ask) / 2.0
        buy_price = max(mid - (min_target_spread / 2.0), 0.00000001)
        sell_price = max(mid + (min_target_spread / 2.0), buy_price + 0.00000001)
    else:
        buy_price, sell_price = build_prices(target_price, spread, highest_bid, lowest_ask)

    # Keep a small guard from top-of-book edges to avoid crossing/touching too tightly.
    buy_ceiling = max(highest_bid * (1.0 - edge_buffer_pct), 0.00000001)
    sell_floor = max(lowest_ask * (1.0 + edge_buffer_pct), 0.00000001)

    buy_price = min(buy_price, buy_ceiling)
    sell_price = max(sell_price, sell_floor)

    if sell_price <= buy_price:
        return None, None

    effective_spread_pct = (sell_price - buy_price) / buy_price if buy_price > 0 else 0.0
    if effective_spread_pct < min_profit_spread_pct:
        return None, None

    return buy_price, sell_price


def has_similar_open_order(open_orders, order_type, price, tolerance_pct):
    if price <= 0:
        return False
    for order in open_orders:
        if order.get("type") != order_type:
            continue
        existing_price = safe_float(order.get("price"))
        if existing_price <= 0:
            continue
        delta = abs(existing_price - price) / price
        if delta <= tolerance_pct:
            return True
    return False


def get_locked_amounts(open_orders):
    locked_quote = 0.0
    locked_token = 0.0
    for order in open_orders:
        qty = safe_float(order.get("quantity"))
        price = safe_float(order.get("price"))
        if qty <= 0 or price <= 0:
            continue
        if order.get("type") == "buy":
            locked_quote += qty * price
        elif order.get("type") == "sell":
            locked_token += qty
    return locked_quote, locked_token


def get_rc_percent(account_name, active_key, nodes):
    try:
        hive = Hive(keys=[active_key], node=nodes)
        acct = hive.get_account(account_name)
        manabar = acct.get_rc_manabar()
        current_pct = safe_float(manabar.get("current_pct"))
        return max(current_pct, 0.0)
    except Exception as exc:
        print(f"[WARN] RC lookup failed: {exc}")
        return None


def main():
    root = os.path.dirname(__file__)
    config = normalize_config(load_config(root))

    account = (config.get("account_name") or "").strip()
    active_key = (config.get("active_key") or "").strip()
    token = (config.get("token") or "ARBITER").strip()
    quote = (config.get("quote_token") or "SWAP.HIVE").strip()
    target_price = config.get("target_price")
    spread = config.get("spread")
    sell_percentage = config.get("sell_allocation_pct")
    buy_back_percentage = config.get("buy_allocation_pct")
    min_quote_for_buy = config.get("min_quote_for_buy")
    min_profit_spread_pct = config.get("min_profit_spread_pct")
    edge_buffer_pct = config.get("edge_buffer_pct")
    duplicate_price_tolerance_pct = config.get("duplicate_price_tolerance_pct")
    min_rc_percent = config.get("min_rc_percent")
    low_rc_cycle_seconds = config.get("low_rc_cycle_seconds")
    token_reserve = config.get("initial_token_reserve")
    reserve_growth_per_cycle = config.get("reserve_growth_per_cycle")
    cycle_seconds = config.get("cycle_seconds")
    cancel_every_n_cycles = config.get("cancel_every_n_cycles")
    hive_nodes = config.get("hive_nodes")

    if not account or not active_key or not token or target_price <= 0:
        print("[ERROR] Invalid bot_config.json. account_name, active_key, token, and target_price are required.")
        raise SystemExit(1)

    cycle = 0

    print(f"[BOOT] Starting market-maker for {token}/{quote} on account {account}")
    print(f"[BOOT] Target price={target_price} {quote}, spread={spread}")

    while True:
        cycle += 1
        token_reserve += reserve_growth_per_cycle

        print(f"\n[{datetime.utcnow().isoformat()}Z] Cycle {cycle}")

        rc_pct = get_rc_percent(account, active_key, hive_nodes)
        if rc_pct is not None and rc_pct < min_rc_percent:
            print(f"[RC] Low RC detected ({rc_pct:.2f}% < {min_rc_percent}%). Slowing cycle.")
            time.sleep(low_rc_cycle_seconds)
            continue

        orderbook = get_orderbook_top(token)
        if not orderbook:
            print("[WARN] No orderbook data available; sleeping.")
            time.sleep(cycle_seconds)
            continue

        highest_bid = safe_price(orderbook.get("highestBid"))
        lowest_ask = safe_price(orderbook.get("lowestAsk"))

        if highest_bid <= 0 or lowest_ask <= 0:
            print(f"[WARN] Invalid spread: bid={highest_bid}, ask={lowest_ask}")
            time.sleep(cycle_seconds)
            continue

        buy_price, sell_price = build_guarded_prices(
            target_price,
            spread,
            highest_bid,
            lowest_ask,
            edge_buffer_pct,
            min_profit_spread_pct,
        )
        if buy_price is None or sell_price is None:
            print("[GUARD] Price guard rejected cycle (tight/inverted/unprofitable spread).")
            time.sleep(cycle_seconds)
            continue

        open_orders = get_open_orders(account, token=token)
        locked_quote, locked_token = get_locked_amounts(open_orders)

        token_balance = get_balance(account, token)
        hive_balance = get_balance(account, quote)

        available_quote = max(hive_balance - locked_quote, 0.0)
        available_token = max(token_balance - locked_token, 0.0)

        available_to_sell = max(available_token - token_reserve, 0.0)
        sell_qty = round(available_to_sell * sell_percentage, 8)

        if sell_qty > 0 and not has_similar_open_order(
            open_orders,
            "sell",
            sell_price,
            duplicate_price_tolerance_pct,
        ):
            print(f"[SELL] qty={sell_qty} {token} @ {sell_price}")
            place_order(
                account,
                token,
                sell_price,
                sell_qty,
                order_type="sell",
                active_key=active_key,
                nodes=hive_nodes,
            )
        else:
            print(f"[SELL] Skipped (available_token={available_token}, reserve={token_reserve}, duplicate_check=true)")

        hive_to_spend = available_quote * buy_back_percentage
        buy_qty = round(hive_to_spend / buy_price, 8) if buy_price > 0 else 0.0

        if (
            hive_to_spend >= min_quote_for_buy
            and buy_qty > 0
            and not has_similar_open_order(
                open_orders,
                "buy",
                buy_price,
                duplicate_price_tolerance_pct,
            )
        ):
            print(f"[BUY] qty={buy_qty} {token} @ {buy_price} using {hive_to_spend:.8f} {quote}")
            place_order(
                account,
                token,
                buy_price,
                buy_qty,
                order_type="buy",
                active_key=active_key,
                nodes=hive_nodes,
            )
        else:
            print(f"[BUY] Skipped (available_quote={available_quote}, min={min_quote_for_buy}, duplicate_check=true)")

        if cancel_every_n_cycles > 0 and cycle % cancel_every_n_cycles == 0:
            print("[CANCEL] Refreshing one old order")
            run_cancel_once(os.path.join(root, "arbiter_cancel.py"))

        print(f"[SLEEP] Waiting {cycle_seconds}s")
        time.sleep(cycle_seconds)


if __name__ == "__main__":
    main()

🤖 PeakeBot — Autonomous Trading System (RC-AWARE)

Independent multi-token trading bot featuring:
RC-aware execution, adaptive delay logic, and 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

🙏 Acknowledgements

Thanks to and please follow:
@enginewitty @ecoinstant @neoxian @txracer @thecrazygm @holdonia @aggroed

For their continued support, guidance, and help expanding the PeakeCoin ecosystem.