Inside a BTC ETH Ratio Trading Bot: Full Trading Logic, Pseudocode Breakdown, and Why This Algorithmic Trading Framework Is

Cryptocurrency pairs trading has quietly become one of the most requested strategies among quant-curious retail traders, and for good reason: instead of betting on the direction of Bitcoin or Ethereum in isolation, a BTC ETH ratio trading bot bets on the relative strength of one crypto asset versus the other. This article breaks down, in detailed pseudocode form, the complete trading logic of a real-world Python-based algorithmic trading bot — bot_btc_eth_ratio.py — built to trade the Bitcoin-to-Ethereum ratio using CME micro futures contracts (BTCM6) with Ethereum price data pulled in via Redis.

We'll walk through every major subsystem: market data ingestion, volatility regime detection, dynamic position sizing, multi-stage profit-taking, trailing stops, circuit breakers, and session-level risk controls. We'll also explain why this kind of crypto ratio trading strategy pseudocode is an ideal drop-in module for a multi-strategy trading bot framework like the one sold on HFTCode.com, and why the pricing on that entire product line is about to increase — meaning now is the time to acquire it as a base for your own build.

Whether you're searching for "BTC ETH ratio trading bot," "pairs trading algorithm Python," "crypto statistical arbitrage bot," or "algorithmic trading bot pseudocode," this deep dive is written to be the most comprehensive breakdown available.

What Is a BTC ETH Ratio (Pairs) Trading Strategy?

A ratio trade — sometimes called a pairs trading strategy or relative value trade — does not ask "will Bitcoin go up?" It asks a narrower, more statistically tractable question: "will Bitcoin outperform Ethereum, or vice versa, over the next several minutes to hours?"

The bot computes a continuously updating ratio:

ratio = BTC_price / ETH_price

When BTC is strengthening relative to ETH, the ratio rises. When ETH is strengthening relative to BTC, the ratio falls. The entire crypto ratio trading bot is built around detecting momentum, breakouts, and mean-reversion behavior in this ratio series rather than in either asset's raw price — which is a classic statistical arbitrage and pairs trading technique borrowed from institutional equity long/short desks and adapted here for BTC and ETH futures.

This is a market-neutral-leaning approach: because the bot is simultaneously long one leg and short the other (synthetically, via the ratio), it is partially insulated from broad crypto market direction and instead captures the spread between the two largest digital assets.

High-Level Architecture of the BTC ETH Ratio Bot

Before diving into the pseudocode, it's worth understanding the plumbing. This Python trading bot is built on an event-driven architecture:

• Market data ingestion: BTC futures (BTCM6 on CME) ticks arrive through a Rithmic-to-Redis event bus. Ethereum data arrives two ways — through the same event stream, and as a fallback, through a Redis polling loop that scans a set of candidate keys (market_data:ETH, md:ETH, tick:ETH, etc.) every 60 seconds.

• State management: rolling deques track BTC prices, ETH prices, the ratio series, ratio returns, bid/ask spreads, volumes, true ranges, and ATR history — all capped at fixed lengths (600 or 300 bars) to keep memory bounded.

• Signal engine: moving averages, RSI, MACD, Donchian channel breakouts, VWAP z-scores, and a synthetic volatility index all feed into a binary long/short decision gate.

• Simulated (paper) position management: entries, three-stage scaled exits, trailing stops, thesis-invalidation exits, and time-based exits.

• Risk governance layer: dynamic daily/weekly loss limits, consecutive-loss circuit breakers, CME maintenance-window blackouts, and stale-feed protection.

Now let's convert each of these subsystems into clean, strategy-level pseudocode.

Full Pseudocode Breakdown of the BTC ETH Ratio Bot's Trading Logic

1. Bot Initialization Pseudocode

INITIALIZE BTCETHRatioBot:

    SET bot_name, symbol = "BTCM6", exchange = "CME"

    SET strategy_name = "BTC vs ETH Crypto Ratio Trade"

    SET direction = LONG_OR_SHORT

    SET max_contracts = 2

    SET simulated_position = 0

    SET simulated_entry_price = NONE

    SET cumulative_pnl = 0.0

    SET btc_price, btc_bid, btc_ask, btc_volume = 0

    SET eth_price, eth_bid, eth_ask, eth_volume = 0

    CREATE rolling_window btc_prices(max=600)

    CREATE rolling_window eth_prices(max=600)

    CREATE rolling_window ratio_prices(max=600)

    CREATE rolling_window ratio_returns(max=600)

    CREATE rolling_window spreads(max=300)

    CREATE rolling_window volumes(max=300)

    CREATE rolling_window true_ranges(max=14)

    CREATE rolling_window atr_history(max=200)

    SET ema_fast = NONE, ema_slow = NONE, macd_signal = NONE, macd_hist = 0

    SET entry_btc, entry_eth, entry_ratio = NONE

    SET position_qty = 0, position_side = NONE

    SET stop_ratio, target_ratio_1, target_ratio_2, target_ratio_3 = NONE

    SET trailing_stop_ratio, best_ratio_since_entry = NONE

    SET bars_held = 0

    LOAD account_capital FROM environment (default 100000)

    LOAD btc_point_value, eth_point_value FROM environment

    CONNECT redis_client TO redis_url

This initialization block establishes every state variable the bot needs to track both legs of the pair trade — BTC and ETH — independently, plus the derived ratio series that drives all trading decisions.

2. Market Data Ingestion Pseudocode

Each incoming tick is normalized and routed based on which asset it represents:

FUNCTION on_market_data(tick):

    PARSE tick into JSON if needed

    IF tick is not a valid dictionary: RETURN

    symbol = EXTRACT symbol field from tick (checking multiple possible field names)

    price = EXTRACT price (checking price, last, trade_price, close fields)

    IF price is NULL or price <= 0:

        RETURN   # reject bad/zero ticks before touching any state

    bid, ask = EXTRACT bid/ask (fallback to price if missing or crossed)

    volume = EXTRACT volume

    IF symbol contains "ETH" OR symbol == "MET":

        UPDATE eth_price, eth_bid, eth_ask, eth_volume

        APPEND price TO eth_prices

        RETURN

    IF symbol does not contain "BTC" AND symbol not in ("MBT", SYMBOL):

        RETURN   # ignore irrelevant symbols

    UPDATE btc_price, btc_bid, btc_ask, btc_volume

    APPEND btc_price TO btc_prices

    APPEND volume TO volumes

    APPEND (ask - bid) TO spreads

    IF eth_price <= 0: RETURN   # need both legs before computing ratio

    ratio_now = btc_price / eth_price

    IF ratio_now <= 0: RETURN

    APPEND ratio_now TO ratio_prices

    APPEND ((ratio_now - previous_ratio) / previous_ratio) TO ratio_returns

This is the heart of the crypto ratio trading logic: the bot refuses to compute a ratio until it has fresh, valid prices for both BTC and ETH, guarding against the classic pairs-trading pitfall of trading on a stale or zeroed leg.

3. Redis ETH Fallback Polling Pseudocode

Because BTC arrives via the primary Rithmic feed but ETH may lag, a background async task continuously checks Redis snapshots as a safety net:

ASYNC FUNCTION poll_eth_snapshot_loop():

    possible_keys = ["market_data:ETH", "market_data:MET", "md:ETH",

                      "md:MET", "tick:ETH", "tick:MET"]

    WHILE bot is running:

        FOR key IN possible_keys:

            raw = redis_client.GET(key)

            IF raw exists:

                payload = PARSE JSON(raw)

                IF payload symbol contains "ETH" or "MET":

                    UPDATE eth_price/bid/ask/volume from payload

                    APPEND price TO eth_prices

                    BREAK

        SLEEP(60 seconds)

4. Average True Range (ATR) Calculation Pseudocode

The bot computes a custom ATR based on the BTC leg's bid/ask range and the previous close, feeding volatility-adjusted stop distances:

FUNCTION update_atr():

    prev_close = second-to-last btc_price (or current if unavailable)

    true_range = MAX(

        ask - bid,

        ABS(ask - prev_close),

        ABS(bid - prev_close)

    )

    APPEND true_range TO true_ranges (max 14 bars)

    atr = MEAN(true_ranges)

    APPEND atr TO atr_history

    RETURN atr

5. MACD-on-Ratio Pseudocode

Rather than computing MACD on raw BTC or ETH prices, the bot computes it directly on the ratio series — a key differentiator of this relative-value trading strategy:

FUNCTION update_macd(ratio_now):

    ema_fast = EXPONENTIAL_MOVING_AVERAGE(ema_fast, ratio_now, period=12)

    ema_slow = EXPONENTIAL_MOVING_AVERAGE(ema_slow, ratio_now, period=26)

    macd_line = ema_fast - ema_slow

    macd_signal = EXPONENTIAL_MOVING_AVERAGE(macd_signal, macd_line, period=9)

    macd_hist = macd_line - macd_signal

    RETURN macd_hist

6. Synthetic Volatility Regime ("Synthetic VIX") Pseudocode

One of the most distinctive parts of this bot is its home-grown volatility index, used to dynamically resize positions and widen/narrow stop distances:

FUNCTION compute_volatility_regime():

    current_vol = STDDEV(last 60 ratio_returns) IF enough data ELSE 0

    baseline_vol = STDDEV(all ratio_returns) IF enough data ELSE current_vol

    IF baseline_vol <= 0: baseline_vol = current_vol OR tiny_epsilon

    synthetic_vix = (current_vol / baseline_vol) * 20

    synthetic_vix = MAX(synthetic_vix, 10)

    IF synthetic_vix > 35:

        atr_multiplier = 3.0 ; size_adjustment = 0.25

    ELSE IF synthetic_vix > 25:

        atr_multiplier = 2.5 ; size_adjustment = 0.50

    ELSE IF synthetic_vix > 15:

        atr_multiplier = 2.5 ; size_adjustment = 0.75

    ELSE:

        atr_multiplier = 2.0 ; size_adjustment = 1.0

    RETURN current_vol, baseline_vol, synthetic_vix, atr_multiplier, size_adjustment

This gives the bot a self-referential, regime-aware risk dial: when the ratio is moving more violently than its own historical norm, position sizes shrink automatically and stop distances widen — a hallmark of a mature algorithmic risk management framework.

7. Dynamic Loss Limits and Circuit Breaker Pseudocode

FUNCTION update_risk_limits():

    effective_point_value = ABS(btc_point_value btc_price) + ABS(eth_point_value eth_price)

    session_risk_multiplier = MAX(1.0, synthetic_vix / 20.0)

    daily_loss_limit = -1 MAX(current_vol, atr, epsilon)

                        MAX(1, max_contracts) *

                        effective_point_value *

                        session_risk_multiplier

    weekly_from_daily = daily_loss_limit * 2

    weekly_from_capital = -1 (account_capital 0.10)

    weekly_loss_limit = MIN(weekly_from_daily, weekly_from_capital)

FUNCTION check_circuit_breakers():

    IF cumulative_pnl <= daily_loss_limit:

        ACTIVATE circuit_breaker

        SET cooldown_until = start_of_next_session

    IF cumulative_pnl <= weekly_loss_limit:

        ACTIVATE circuit_breaker

        SET cooldown_until = start_of_next_session

    IF consecutive_losses >= 5:

        ACTIVATE circuit_breaker

        SET cooldown_until = start_of_next_session

    IF circuit_breaker_active AND now >= cooldown_until:

        DEACTIVATE circuit_breaker

        RESET consecutive_losses

This circuit breaker trading logic is a professional-grade safety mechanism rarely found in retail trading bot tutorials — it protects capital across three independent dimensions: daily drawdown, weekly drawdown, and losing-streak psychology.

8. CME Maintenance Window & Stale Feed Guard Pseudocode

FUNCTION is_cme_maintenance(now_utc):

    convert now_utc to Chicago time (handling DST approximation)

    IF weekday IN (Mon, Tue, Wed, Thu) AND hour is between 16:00-17:00 CT:

        RETURN TRUE

    IF weekday == Friday AND hour is between 15:00-17:00 CT:

        RETURN TRUE

    RETURN FALSE

FUNCTION check_feed_staleness(now):

    stale_btc = (now - last_btc_tick_time) > 2x poll_interval

    stale_eth = (now - last_eth_tick_time) > 2x poll_interval

    RETURN stale_btc, stale_eth

No new trades are allowed to open during the daily CME futures maintenance window, nor when either the BTC or ETH data feed is stale — a critical defensive layer for any futures trading bot.

9. Signal Generation Pseudocode: RSI, Donchian, VWAP Z-Score

FUNCTION compute_rsi(ratio_prices, period):

    gains = SUM of positive changes over last `period` bars

    losses = SUM of negative changes (absolute value) over last `period` bars

    IF losses == 0: RETURN 100 IF gains > 0 ELSE 50

    relative_strength = (gains/period) / (losses/period)

    RETURN 100 - (100 / (1 + relative_strength))

FUNCTION compute_signals():

    fast_ma = MEAN(last fast_n ratio_prices)

    slow_ma = MEAN(last slow_n ratio_prices)

    donchian_high = MAX(prior ratio window, excluding current bar)

    donchian_low = MIN(prior ratio window, excluding current bar)

    volume_confirm = current_btc_volume > average_volume

    ratio_vwap = VOLUME_WEIGHTED_AVERAGE(ratio_prices, volumes)

    ratio_stddev = STDDEV(ratio_prices)

    zscore = (ratio_now - ratio_vwap) / ratio_stddev IF ratio_stddev > 0 ELSE 0

    RETURN fast_ma, slow_ma, donchian_high, donchian_low, volume_confirm, zscore

10. Position Sizing Pseudocode

FUNCTION compute_position_size():

    rr_ratio = compute_risk_reward_ratio()   # based on historical win rate + Sharpe estimate

    stop_distance = atr * atr_multiplier

    risk_percent = CLAMP(0.02 * (avg_historical_atr / current_atr), between 0.01 and 0.02)

    vol_adjustment = avg_historical_atr / current_atr

    raw_contracts = (account_capital * risk_percent) /

                     (stop_distance * effective_point_value)

    raw_contracts *= volatility_size_adjustment

    raw_contracts *= vol_adjustment

    qty = FLOOR(raw_contracts)

    qty = MIN(qty, max_contracts)

    rr_valid = (stop_distance rr_ratio) >= (stop_distance 2.0)

    RETURN qty, rr_valid, stop_distance, rr_ratio

This translates account equity, live volatility, and strategy quality (rolling reward-to-risk ratio) directly into contract size — a form of volatility-adjusted position sizing similar to what institutional risk desks use.

11. Entry Logic Pseudocode

FUNCTION check_entry_conditions():

    IF position != 0: RETURN   # only evaluate entries when flat

    IF circuit_breaker_active OR in_maintenance_window OR spread_too_wide

       OR stale_btc OR stale_eth:

        RETURN   # blocked by risk gates

    btc_return = pct_change(last two btc_prices)

    eth_return = pct_change(last two eth_prices)

    btc_outperforming = btc_return > eth_return

    LONG_SIGNAL = (

        ratio_now > fast_ma AND

        ratio_now > slow_ma AND

        rsi > 50 AND

        macd_hist > 0 AND

        volume_confirm AND

        ratio_now >= donchian_high AND

        zscore >= 0 AND

        btc_outperforming

    )

    SHORT_SIGNAL = (

        ratio_now < fast_ma AND

        ratio_now < slow_ma AND

        rsi < 50 AND

        macd_hist < 0 AND

        volume_confirm AND

        ratio_now <= donchian_low AND

        zscore <= 0 AND

        NOT btc_outperforming

    )

    IF rr_valid AND 1 <= qty <= max_contracts:

        IF LONG_SIGNAL: ENTER("BUY", qty, ratio_now, stop_distance, rr_ratio)

        ELSE IF SHORT_SIGNAL: ENTER("SELL", qty, ratio_now, stop_distance, rr_ratio)

Notice the strategy requires eight simultaneous confirmations before entering — trend alignment (fast/slow MA), momentum (RSI, MACD), volume confirmation, breakout confirmation (Donchian channel), statistical deviation (VWAP z-score), and cross-asset confirmation (BTC actually outperforming ETH on the most recent tick). This multi-factor gating is designed to dramatically reduce false signals compared to a single-indicator crossover system.

12. Trade Entry Execution Pseudocode

FUNCTION simulate_entry(side, qty, ratio_now, stop_distance, rr_ratio):

    entry_price = ask IF side == BUY ELSE bid  (fallback to last price)

    RECORD entry_btc, entry_eth, entry_ratio, position_qty, position_side

    IF side == BUY:

        position = +qty

        stop_ratio = ratio_now - stop_distance

        target_1 = ratio_now + stop_distance

        target_2 = ratio_now + stop_distance * MIN(rr_ratio, 2.0)

        target_3 = ratio_now + stop_distance * rr_ratio

    ELSE:

        position = -qty

        stop_ratio = ratio_now + stop_distance

        target_1 = ratio_now - stop_distance

        target_2 = ratio_now - stop_distance * MIN(rr_ratio, 2.0)

        target_3 = ratio_now - stop_distance * rr_ratio

    trailing_stop_ratio = stop_ratio

    LOG entry event with all levels

13. Position Management Pseudocode: Trailing Stop + Scale-Out Targets

FUNCTION manage_open_position(ratio_now, atr, atr_multiplier):

    IF position == 0: RETURN

    bars_held += 1

    stop_distance = atr * atr_multiplier

    # --- Trailing stop update ---

    IF position > 0:

        best_ratio = MAX(best_ratio, ratio_now)

        trail_multiplier = MAX(1.0, atr_multiplier - (unrealized_pnl / account_capital))

        candidate_stop = best_ratio - (atr * trail_multiplier)

        trailing_stop_ratio = MAX(trailing_stop_ratio, candidate_stop)

        stop_ratio = MAX(stop_ratio, trailing_stop_ratio)

    ELSE:

        best_ratio = MIN(best_ratio, ratio_now)

        trail_multiplier = MAX(1.0, atr_multiplier - (unrealized_pnl / account_capital))

        candidate_stop = best_ratio + (atr * trail_multiplier)

        trailing_stop_ratio = MIN(trailing_stop_ratio, candidate_stop)

        stop_ratio = MIN(stop_ratio, trailing_stop_ratio)

    # --- Scale-out exit sizing: 50% / 25% / 25% ---

    qty_total = ABS(position)

    q1 = MAX(1, FLOOR(qty_total * 0.5))

    q2 = FLOOR(qty_total * 0.25)

    q3 = qty_total - q1 - q2

    IF position > 0:

        IF NOT stage1_done AND ratio_now >= target_1: EXIT(q1, "TARGET_1R")

        IF NOT stage2_done AND ratio_now >= target_2: EXIT(q2, "TARGET_2R")

        IF NOT stage3_done AND ratio_now >= target_3: EXIT(q3, "TARGET_3R")

        IF ratio_now <= stop_ratio: EXIT(remaining_qty, "STOP_HIT")

    ELSE:

        IF NOT stage1_done AND ratio_now <= target_1: EXIT(q1, "TARGET_1R")

        IF NOT stage2_done AND ratio_now <= target_2: EXIT(q2, "TARGET_2R")

        IF NOT stage3_done AND ratio_now <= target_3: EXIT(q3, "TARGET_3R")

        IF ratio_now >= stop_ratio: EXIT(remaining_qty, "STOP_HIT")

    # --- Time-based exit ---

    IF bars_held >= holding_period_bars: EXIT(remaining_qty, "TIME_EXIT")

    # --- Thesis invalidation ---

    IF fast_ma crosses below slow_ma OR macd_hist < 0 OR volume_collapse:

        IF position > 0: EXIT(remaining_qty, "THESIS_INVALIDATED")

    IF fast_ma crosses above slow_ma OR macd_hist > 0 OR volume_collapse:

        IF position < 0: EXIT(remaining_qty, "THESIS_INVALIDATED")

This is a genuinely sophisticated exit strategy pseudocode: three progressive profit-taking stages at 1R, 2R, and 3R multiples of risk, a volatility-adjusted trailing stop that tightens as unrealized profit grows, a maximum holding period (dynamically shortened during high-volatility regimes), and a thesis-invalidation exit that closes the trade the moment the original technical rationale breaks down — even before the stop or target is hit.

14. Trade Exit and P&L Calculation Pseudocode

FUNCTION simulate_exit(qty, reason, ratio_now):

    IF position == 0 OR qty <= 0: RETURN

    qty = MIN(qty, ABS(position))

    exit_price = bid IF position > 0 ELSE ask

    btc_leg_pnl = (btc_price - entry_btc) btc_point_value qty

    eth_leg_pnl = (entry_eth - eth_price) eth_point_value qty

    pnl = btc_leg_pnl + eth_leg_pnl

    IF position < 0: pnl = -pnl

    RECORD trade in trade_history

    cumulative_pnl += pnl

    IF pnl < 0: consecutive_losses += 1

    ELSE: consecutive_losses = 0

    REDUCE position by qty

    IF position == 0: RESET all trade-state variables

The pair P&L formula is worth highlighting: profit comes from BTC gaining while ETH loses (for a long-ratio trade), or the reverse for a short-ratio trade — this is the literal mechanical expression of a market-neutral pairs trade.

15. Session-Level Profit Lock Pseudocode

FUNCTION check_session_profit_target():

    quality_score = CLAMP((rr_ratio - 2.0) / rr_ratio, between 0 and 1)

    profit_target_pct = 0.03 + (0.02 * quality_score)   # dynamic 3%-5% target

    profit_target_usd = account_capital * profit_target_pct

    IF cumulative_pnl >= profit_target_usd:

        IF position != 0: EXIT(remaining_qty, "ACCOUNT_PROFIT_TARGET_HIT")

        ACTIVATE circuit_breaker

        SET cooldown_until = next_session_start

Once the bot locks in its daily profit objective — dynamically scaled between 3% and 5% of account capital based on the trailing quality of its own signal (reward-to-risk ratio) — it stops trading for the session. This "quit while you're ahead" governor is a distinguishing feature versus naive bots that keep trading until they give profits back.

Why This Pseudocode Matters for Algorithmic Traders

Stepping back from the line-by-line breakdown, several design principles stand out that make this BTC ETH ratio bot a strong reference architecture for anyone building a crypto algorithmic trading system:

1. Dual-asset synchronization — the bot refuses to act until both legs of the pair have fresh data, avoiding the single biggest bug class in pairs trading bots.

2. Self-referential volatility scaling — rather than hardcoding stop distances, the synthetic VIX regime detector adapts position size and stop width to current market conditions relative to the bot's own historical baseline.

3. Layered risk governance — daily loss limit, weekly loss limit, consecutive-loss breaker, maintenance-window blackout, and stale-feed protection all operate independently and simultaneously.

4. Multi-stage exits — scaling out at 1R/2R/3R plus a trailing stop plus a thesis-invalidation exit gives the strategy several independent ways to lock in gains or cut losses, rather than a single binary stop/target.

5. Dynamic reward-to-risk feedback loop — the bot recalculates its own historical win rate and P&L Sharpe estimate continuously and feeds that back into position sizing and profit targets.

This kind of layered, self-adjusting risk management pseudocode is exactly the sort of architecture that separates a toy trading script from a production-grade paper trading bot framework — and it's precisely the type of module that's valuable as a base for larger multi-strategy systems.

Using This BTC ETH Ratio Pseudocode as a Base Strategy for a Multi-Bot Trading Hub

Here's where this pseudocode becomes especially useful beyond a single-symbol Rithmic/CME deployment: it can be adapted as an additional strategy module inside a broader multi-strategy trading bot hub — specifically the kind of architecture sold as the IBKR Trading Bot Hub on HFTCode.com.

That product is built around a hub-and-spoke architecture in which all bots connect to the hub server, and the hub server manages the single connection to TWS, receiving order requests from the bots and translating them into IBKR API calls before routing responses back. This "traffic controller" design is deliberately similar to how institutional trading desks operate — one gateway managing many bot connections is the same approach used by institutional trading desks that need to route orders from multiple strategy teams through a single broker connection.

Because the BTC ETH ratio bot's pseudocode is already organized into clean, modular functions — data ingestion, volatility regime detection, signal generation, position sizing, entry/exit management, and risk governance — it maps naturally onto a hub architecture that already ships with strategies like NVDA/BHP SMA crossovers, EUR/USD RSI, and XAUUSD Bollinger Bands. Here's how you could reuse this exact trading logic as a new bot inside that hub:

• Swap the data source, keep the math. Replace the Rithmic/Redis tick handler with the hub's existing IBKR/TWS market data callback, but keep the ratio calculation, ATR-on-ratio, MACD-on-ratio, RSI, Donchian breakout, and VWAP z-score logic untouched — the statistical core is broker-agnostic pseudocode.

• Reuse the volatility regime detector as a shared utility. The synthetic VIX / ATR-multiplier / position-size-adjustment function is generic enough to apply to any pair — BTC ETH futures, two correlated equities, or two FX crosses — making it a strong candidate for a shared risk-utilities module across every bot in the hub.

• Port the circuit breaker and maintenance-window logic directly. Daily/weekly loss limits, consecutive-loss shutoffs, and exchange maintenance blackout windows are exactly the kind of governance layer that should sit at the hub level, protecting all bots simultaneously rather than being reimplemented per strategy.

• Add Claude AI as a signal filter on top. Since the hub already integrates a Claude AI filter layer, the eight-condition long/short signal block from this pseudocode is a natural candidate for an additional AI-based confirmation pass — for example, having Claude review the RSI/MACD/Donchian/z-score confluence before authorizing the trade, adding a qualitative sanity check on top of the quantitative gate.

• Extend the scale-out/trailing-stop engine hub-wide. The 1R/2R/3R progressive exit plus volatility-adjusted trailing stop is more sophisticated than many single-indicator strategies use by default, and could become the hub's standard exit-management template for every bot, not just this ratio trade.

This is precisely the value proposition of buying a production-ready trading bot framework rather than building from zero: you get the multiplexed broker connection, the strategy scaffolding, and the AI filtering layer already solved, and you spend your engineering time adapting proven pseudocode — like the ratio-trading logic broken down above — into new modules rather than reinventing plumbing.

Why the Price of This Trading Bot Framework Is About to Increase

If you've been eyeing the IBKR Trading Bot Hub or its bundled version with AI interview preparation, there's a good reason to move sooner rather than later. As of now, the standalone Trading Bot Hub and the Hub + AI Interview Preparation bundle are listed at introductory pricing — the IBKR Trading Bot Hub sells for $47.00, while the Trading Bot Hub + AI Interview Preparation Platform bundle sells for $67.00. Both of these figures sit far below where a recent internal ad-pricing evaluation says these products should actually be positioned once they scale into paid advertising.

That evaluation broke the two offerings into fundamentally different pricing categories:

The standalone Trading Bot Hub is being treated as a tool, not a transformation. On paid social platforms like Meta, developer tools and trading bots historically plateau well below $300 in cold-traffic conversion, because prospects mentally compare them to free GitHub repositories or $30 Udemy courses — even when the actual product represents enterprise-grade architecture like single-TWS-connection multiplexing across unlimited bots. The recommended ad-optimized price range for this category is $147–$297, with $197 identified as the sweet spot: impulse-friendly enough to convert casual buyers, yet high enough to filter for serious builders.

The bundled Trading Bot Hub + AI Interview Preparation Platform, on the other hand, is being repositioned entirely — not as a coding tool, but as a career outcome. Because the bundle adds 500+ codebase-grounded interview questions, firm-specific interview modules for quant shops like Citadel, Two Sigma, RenTech, and Jump, plus compensation intelligence spanning $250k–$400k junior roles up to $5M+ principal-level packages, the pricing conversation shifts from "what's a trading bot worth?" to "what's a shot at a $250k+ quant offer worth?" The recommended ad price range here is $497–$997, with $697 as the target sweet spot — deliberately staying under the psychological $1,000 barrier while still signaling premium positioning.

In other words, the $47 and $67 price points currently live on the product pages are almost certainly temporary, pre-scale pricing that will be revised upward toward those ad-optimized benchmarks as the products move into broader paid promotion. For anyone planning to use either the standalone hub or the bundle as the base infrastructure for their own strategies — including a ratio-trading module like the one detailed in this article — acquiring it now, before the repricing takes effect, locks in significantly more value per dollar than waiting.

Key Takeaways: BTC ETH Ratio Bot Pseudocode Summary

To recap the full trading logic covered in this algorithmic trading bot pseudocode breakdown:

• The bot computes a live BTC ETH price ratio and applies momentum, breakout, and mean-reversion indicators (MACD, RSI, Donchian channels, VWAP z-score) directly to that ratio series rather than to either raw asset.

• A synthetic volatility index, self-calibrated against the strategy's own historical volatility baseline, dynamically adjusts both position size and stop-loss distance.

• Entries require eight simultaneous confirmations spanning trend, momentum, volume, breakout, statistical deviation, and cross-asset relative performance.

• Exits are managed through a three-stage scale-out (1R/2R/3R), a volatility-adjusted trailing stop, a maximum holding period, and a thesis-invalidation check — giving the position multiple independent, overlapping ways to close profitably or defensively.

• A multi-layer risk governance system — daily loss limits, weekly loss limits, consecutive-loss breakers, exchange maintenance blackouts, and stale-feed protection — sits above the entry/exit engine to protect capital at the account level.

• A dynamic session profit lock (3%–5% of account capital, scaled by strategy quality) stops the bot from over-trading once its daily objective is met.

This pseudocode architecture is directly portable as a new strategy module inside a broader multi-strategy Interactive Brokers trading bot hub, where its risk-governance and volatility-adaptive sizing logic can be generalized into shared utilities used across every bot in the fleet — from equity SMA crossovers to FX RSI strategies to commodity Bollinger Band systems.

For traders and developers who want to study or extend this kind of crypto ratio trading strategy, the smartest path is to acquire an existing production-ready trading bot framework as your base — before the pricing on that framework rises to reflect its true positioning as either a professional developer tool or a career-outcome-driving quant preparation platform.