AquaBot Review – Check NOW!
When I first booted up Aqua Bot, it felt like Solana trading finally got a clean, frictionless front end — fast swaps, smooth UX, and a promise to make on-chain trading as easy as texting a friend. But the deeper I dug, the more it became clear: Aqua wasn’t just chasing speed, it was trying to reinvent how trading bots built communities, rewards, and trust.
Then came the rug rumors, the disappearing team, and a hard lesson in how even the best-designed systems can fall apart without transparency.
In this deep dive, I’m peeling back every layer of Aqua Bot — its architecture, performance, token model, and where it stacked up against other power players like Bloom and Maestro. Whether you’re a trader, builder, or just another degen who loves bots that actually work (until they don’t), this is the story of Aqua Bot — from promising launch to cautionary tale.
Aqua Bot positions itself as a Solana-native trading assistant designed to make DEX trading faster, simpler, and more accessible through a Telegram interface.
The team brands it as a “lightning-fast, low-fee” platform where users can execute trades, set limit orders, manage wallets, and eventually earn through token-linked incentives.
It claims to fuse the speed of on-chain execution with the simplicity of a chat-based UI, reducing friction for users who want to buy, sell, and manage positions without using standalone wallets or terminals.
The Problem It Aims to Solve
Trading on Solana DEXs often demands quick reaction times, multiple tool integrations, and a good understanding of RPC behaviors.
Many traders—especially retail users—struggle with transaction delays, failed swaps, or front-running during volatile moments. Aqua Bot aims to fix this by giving traders a compact command center that combines transaction routing, real-time token scanning, and quick-trade execution in one Telegram flow. Its “plug-and-play” onboarding caters to two main audiences:
- Retail traders who want a simple, fast way to trade from Telegram without worrying about technical setup.
- Advanced users who value latency, custom parameters, and private routing for competitive trade execution.
Why This Review Matters
Aqua Bot gained attention because it wasn’t just another Telegram trading bot — it also tied its growth to the AQUA token and a liquidity event marketed as a major community launch.
The project’s documentation emphasized innovation and a “Liquidity Ladder” system meant to provide sustainable, fair token launches. However, controversy arose around the September 2025 presale, when multiple reports surfaced claiming a rug pull and team disappearance.
Given the volatility of Solana’s trading landscape, Aqua Bot’s story reflects both technical ambition and the risks of unverified bot ecosystems. A full review is valuable for understanding its original mechanics, its architectural strengths, and the broader lessons for traders using automated DEX tools.
Architecture & Technical Design
High-Level Architecture
Aqua Bot operates through a Telegram-based front end linked to a backend service layer that interfaces with Solana’s RPC infrastructure. When a user initiates a command—like “buy,” “sell,” or “set limit”—the backend constructs and signs the transaction, routes it through a private RPC endpoint, and returns confirmations within the Telegram chat.
The architecture can be broken down into:
- Frontend: Telegram bot interface with contextual menus, commands, and quick presets.
- Backend: A transaction builder and queue system responsible for preparing and broadcasting signed instructions.
- RPC Layer: Optimized Solana RPC endpoints used for rapid block access and confirmation.
- Protection Layer: Settings like MEV/rug protection, slippage limits, and private broadcast queues to reduce exposure.
This setup mirrors the structure of established bots like BONKbot or Photon, but Aqua emphasized simplified onboarding and an integrated wallet creation system for immediate use.
Supported Chains, Protocols & Networks
At the time of this evaluation, Aqua Bot was primarily Solana-based. The platform referenced potential multi-chain expansion but did not demonstrate active cross-chain support. It interacts with Solana DEXs, token lists, and liquidity providers such as Meteora DLMM for its AQUA token pools.
Key Subsystems
- Event Detection: Monitors new tokens, liquidity changes, and price updates to power quick trading prompts.
- Order Engine: Handles instant swaps and limit orders while managing retries for failed or stuck transactions.
- Wallet Connectors: Automatically generate a default wallet during onboarding and allow manual imports for external wallets.
- Token Infrastructure: Integrates the AQUA token and Liquidity Ladder mechanisms, linking trading activity to token-based rewards and governance.
Handling Chain-Specific Challenges
Solana’s high throughput allows for ultra-fast execution but punishes poorly optimized RPC usage. Aqua claims to solve this through private routing and transaction prioritization to avoid front-running and congestion stalls. It also includes customizable settings for slippage, priority fees, and transaction retries to mitigate failures during high-volume windows.
External Dependencies
Aqua depends on several critical third-party systems:
- Solana RPC Providers for transaction routing and confirmation.
- DEX APIs for liquidity data and swap pricing.
- Bridging or deposit systems to fund new wallets from external networks.
- Meteora for its Liquidity Ladder deployment and AQUA token pools.
While efficient for user experience, this architecture introduces reliance on external infrastructure that must maintain uptime and integrity.
Feature Set & Functionality
Below is an overview of Aqua Bot’s major feature categories, focusing on their design intent, practical behavior, and competitive standing.
Trade Execution
Aqua Bot offers quick swap functionality directly within Telegram. The system claims near-instant execution using private RPC routing and provides confirmation messages showing transaction IDs. Users can buy tokens by address or ticker and sell with adjustable slippage and priority fee settings.
Reliability: Fast in ideal conditions; performance depends on RPC endpoint health.
Limitations: May struggle with illiquid or recently launched tokens due to pool depth.
Comparison: Similar in concept to BONKbot and Photon, with an emphasis on simplicity rather than analytics depth.
Copy Trading / Mirroring
Public materials show limited evidence of fully developed copy-trading features. Aqua seems designed more for individual execution and speed rather than social trading or wallet replication.
Comparison: Lags behind platforms like Maestro or Unibot that feature curated wallet copy systems.
Automation / Trigger-Based Trades
Automation options appear minimal beyond limit orders and quick-execute presets. The documentation hints at MEV protection and auto-retry functions but not fully customizable triggers like “auto-buy on liquidity add.”
Comparison: Behind EVM-based bots such as Maestro and BananaGun, which feature broader event-driven trading.
Limit Orders / Conditional Orders
One of Aqua’s advertised features is limit order support within Telegram. Users can set a target price, and the bot executes when market conditions meet that threshold.
Performance: Works well for liquid pairs; reliability decreases with microcap tokens.
Edge Cases: Missing trailing-stop or multi-tier profit-taking options.
Reward / Loyalty / Points Systems
The AQUA token functions as both a utility and reward mechanism. The Liquidity Ladder model encouraged users to participate in the ecosystem through token holding and engagement.
Strength: Creates a native incentive layer.
Weakness: Vulnerable to speculative manipulation and credibility risks following the presale controversy.
Multi-Chain or Cross-Chain Operations
Aqua advertised multi-chain ambitions but, in practical terms, remained confined to Solana. Users could bridge funds into the bot, but execution was Solana-only.
Comparison: Not competitive yet with multi-chain bots like Bloom or Maestro that support multiple EVM environments.
Referral / Leaderboard / Gamification
Aqua incorporated referral mechanics with fee rebates and token-based rewards. However, its leaderboard features were limited or not widely publicized.
Observation: Gamification existed more through token rewards than social metrics.
UX Elements (Menus, Dashboards, Presets)
The Telegram interface was straightforward, with clean command menus, preset actions (Buy, Sell, Limit, Settings), and quick confirmations. Onboarding automatically generated a wallet, allowing new users to begin trading immediately.
Strength: Extremely fast setup for beginners.
Weakness: No visual web dashboard or advanced analytics for professionals.
Getting Started with Aqua Bot
Step 1: Finding the Bot
Head to the official Telegram link — @AquaioBot.
Once you open it, you’ll see a short welcome message and a Start button. Clicking Start initializes your trading session. Unlike browser-based DEXs, Aqua runs entirely through Telegram, which makes setup instant.
When you start, Aqua automatically generates a non-custodial wallet inside the bot’s encrypted local session. You can also import an existing wallet using a private key or mnemonic phrase, though this introduces custody risks. Always use test funds first until you confirm the wallet management model.
Step 2: Funding Your Wallet
After wallet creation, you’ll receive your Solana address.
You’ll need to send some SOL (for gas and trades) from an external wallet such as Phantom or Solflare.
Once funded, your balance appears directly in chat via inline updates like:
💧 Balance Updated
Wallet: SoL3x…8PqK
Balance: 2.13 SOL
The bot then unlocks the trading menus.
Step 3: Exploring the Menus
Aqua’s main commands are menu-driven and typically include:
- Buy: Enter a token symbol or paste a Solana address. The bot fetches token info, current price, and liquidity pool details.
- Sell: Choose a token from your holdings and specify the amount to sell.
- Limit Order: Set a target price at which the bot should automatically execute your buy/sell.
- Settings: Adjust slippage tolerance, transaction priority fee, and enable or disable MEV protection.
- History / Portfolio: View executed trades and current token balances.
The menu appears as inline buttons, so you can navigate quickly without typing commands.
Step 4: Buying a Token
You can purchase tokens using either a contract address or ticker.
Example command:
/buy BONK
The bot responds with token details, liquidity, and price. You confirm with a button click. Aqua’s backend builds and signs the transaction, then sends it to Solana via a private RPC.
Once confirmed, you’ll get a receipt:
✅ Transaction Successful
Bought 1,000,000 BONK for 0.1 SOL
Tx: solscan.io/tx/2xAq…
This system was designed for sub-2 second execution during low congestion windows.
Step 5: Setting Limit Orders
Aqua allows you to predefine trades at a target price.
You enter:
/limit BONK 0.00000027 SOL
The backend watches the market and executes automatically when the price hits that mark. This feature relies on the bot’s off-chain monitoring, so uptime and backend reliability directly affect success rates.
Step 6: Managing Settings
Inside the Settings menu, you’ll find toggles for:
- Slippage (1%–20%)
- Priority Fees (default, fast, ultra-fast)
- MEV Protection (routes through private RPC)
- Auto-Sell or Auto-Take Profit (if supported)
These define your execution behavior. The settings persist between sessions, letting you maintain a trading profile.
Step 7: Tracking and Withdrawing
You can use /portfolio or /tokens to see all holdings in your bot wallet.
To withdraw, use /withdraw followed by the destination wallet address and token amount.
The bot sends a Solana transaction transferring the assets.
For example:
/withdraw SoL3x…8PqK 1.5 SOL
Step 8: Optional – Referral and Rewards
Aqua featured a referral system tied to its AQUA token. You could generate a personal link and share it with others to earn fee rebates or token rewards when they traded through your link.
Leaderboard features were under development but primarily linked to token activity rather than wallet profit tracking.
Step 9: Security and Caution Notes
- Treat Telegram-based bots as semi-custodial. Even if non-custodial by design, they rely on backend relays.
- Always verify you’re using the official bot handle and not clones.
- Avoid importing wallets holding significant funds. Use new wallets specifically for trading bots.
- Enable Telegram 2FA and never click transaction confirmations from unofficial chats.
Step 10: Exiting or Resetting
If you want to disconnect, you can type /reset or /logout.
This clears your session but does not delete your on-chain wallet; you can still recover it with the seed phrase or export key.
Aqua Bot’s onboarding stands out for being frictionless—a few clicks from “Start” to first trade. It’s beginner-friendly, with built-in wallet creation and Telegram-native execution flow. However, the convenience comes with trade-offs in visibility and security since most confirmations, logs, and metrics live off-chain or behind the bot’s backend.
Performance & Latency
Event Detection Latency
Aqua Bot’s detection layer is tuned for real-time token discovery on Solana. Based on community testing during launch weeks, the bot was able to identify new tokens and liquidity events within roughly 300–800 milliseconds after pool creation.
That’s competitive with leading Solana bots, considering Solana’s high transaction throughput.
However, under heavy network congestion, latency occasionally spiked to 1.5–2 seconds, usually caused by RPC rate limits or failed webhooks from liquidity sources.
Transaction Broadcast Speed
Once a user confirms a swap, Aqua’s backend constructs and signs the transaction before broadcasting via a private RPC relay. This cuts down broadcast lag and mempool exposure.
On normal load, broadcast-to-confirmation was measured at under 1.2 seconds on average — faster than public RPC endpoints.
The system’s efficiency, however, depends on its relay uptime and queue depth. During high-volume events like presales or trending launches, the relay layer can become a single point of congestion.
Fill Rate and Reliability
Aqua achieved high fill success rates (90–95%) under regular market conditions, dropping to around 75–80% when Solana entered high-congestion phases. This puts it in line with Photon and slightly behind BONKbot, which uses distributed RPC clusters.
Failure modes typically involved RPC timeouts, outdated pricing data, or duplicate submission errors that triggered “insufficient funds” or “already processed” messages.
Congestion Handling
When Solana’s mempool jammed, Aqua’s private RPCs and retry mechanism helped maintain operational stability, but transaction queues sometimes delayed secondary orders.
There’s no evidence of dynamic RPC failover — meaning if the main relay went down, users had to wait for the team to reset endpoints.
Solana vs EVM Comparison
At the time of analysis, Aqua operated exclusively on Solana. Its architecture is designed for Solana’s parallel execution model, so direct EVM performance comparison isn’t possible yet.
If EVM expansion happens, transaction latency will likely increase due to gas fee optimization and block confirmation timing.
Infrastructure Resilience
Aqua’s uptime was reported as strong during its first month, but following the September 2025 liquidity event, there were intermittent outages and loss of backend connectivity.
This indicates centralized backend reliance without redundancy — an operational risk for users relying on constant uptime.
Security & Risk Assessment
Threat Model
Aqua’s design exposes users to several categories of risk typical of Telegram-based trading bots:
- MEV and front-running on public RPCs.
- Failed transactions or chain reorgs leading to slippage or partial fills.
- Custodial exposure if wallet keys are stored or transmitted insecurely.
- Phishing and impersonation through fake bot handles or Telegram spoofing.
Risk Mitigation Measures
Aqua introduced basic MEV and rug protection toggles. These work by routing orders through private RPC relays, hiding pending transactions from public mempools.
Users could also adjust slippage, set transaction priority, and enable “duplicate prevention” to avoid double-sends.
However, these protections are only as reliable as the backend. If Aqua’s servers fail or are compromised, the protections lose effect.
Wallet & Key Management
The bot creates a local wallet during setup, which appears non-custodial — meaning users hold the keys in their Telegram session.
However, this model is opaque. Telegram encryption isn’t the same as true client-side key custody. Users must trust Aqua’s backend not to log or intercept key data.
Importing an existing wallet poses higher risk since it exposes your private key to the bot’s environment. Best practice: always fund a new wallet and treat it as temporary.
Smart Contract Risks
Aqua itself isn’t a DeFi protocol but a relay system interacting with existing DEXs.
This minimizes smart contract risk directly tied to the bot, but users are still exposed to DEX-level vulnerabilities like faulty token contracts or rug-pull tokens.
External Dependencies
Aqua relies on Solana RPC providers, Meteora’s DLMM pools for its own token, and various APIs for token and price discovery. Each dependency adds a failure surface — RPC downtime can stall trading, and external oracles could feed stale data.
Transparency and Auditability
Aqua does not provide public smart contract audits or open-source backend code. There’s also no on-chain logging system to verify that trades were executed exactly as claimed.
While the Liquidity Ladder token launch involved a public smart contract, its team’s disappearance following the presale undermines the reliability of any unverified code claims.
Overall Security Rating
- Custody Safety: Medium risk
- Data Transparency: Low
- MEV Protection Effectiveness: Moderate
- User Trust Model: Weak (centralized backend dependency)
Usability & User Experience
Onboarding Flow
Aqua Bot’s onboarding is impressively simple — you open Telegram, click Start, and instantly receive a Solana wallet. No external setup, no browser extensions. This makes it beginner-friendly and fast to use, ideal for impulse traders or users new to DeFi.
Interface Clarity
The Telegram interface is clean, minimal, and responsive. Buttons like Buy, Sell, Limit, Portfolio, and Settings are clearly labeled. Menus are context-sensitive, showing only relevant options based on your wallet status or selected token.
Error Handling and Feedback
Aqua provides inline responses to every action, displaying messages like “Transaction submitted” or “Insufficient liquidity.” When a transaction fails, the bot prompts users to retry or adjust slippage. However, deeper error details (like RPC errors or transaction hashes) are sometimes hidden for simplicity. Advanced users might find this limiting when diagnosing failed trades.
Help and Documentation Quality
The website and token portal offered high-level overviews and marketing visuals but lacked deep technical documentation or troubleshooting guides. Telegram admins handled most questions manually.
For example, there’s no section explaining backend security or detailed wallet management, which creates uncertainty for cautious traders.
Cross-Platform Experience
Aqua is Telegram-exclusive. There’s no separate web dashboard or mobile app outside of Telegram. While this keeps latency low, it limits users who prefer graphical interfaces or performance charts.
Support and Community
The main community hub was the Aqua Telegram group, which was active through early September 2025. Admins responded promptly before the liquidity controversy. After the presale event, community activity dropped sharply, and official responses ceased.
This highlights a dependency on centralized communication — once the team vanished, users had no alternative support channels.
Business Model, Pricing & Incentives
Fee Structure
Aqua Bot built its brand around low trading fees and competitive execution costs.
Transactions typically included a small percentage-based fee on each successful trade, often lower than other Solana bots at the time. The fee was deducted automatically from the executed amount.
While Aqua never released a detailed tiered pricing model, community claims suggested 0.5–1% per trade, depending on referral status.
Premium Features and Subscriptions
Unlike some EVM-based bots (like Maestro or Unibot), Aqua did not offer an explicit “Pro” subscription or gated premium tier. Instead, its ecosystem tied user privileges to AQUA token ownership, meaning that holding the token could potentially lower fees, unlock early access features, or grant governance rights.
Token and Reward Mechanisms
The AQUA token served as the central pillar of the incentive structure. The “Liquidity Ladder” system was marketed as a novel launch mechanism where participants could stake, climb tiers, and earn rewards tied to the bot’s trading volume and future growth.
In theory, this created a positive feedback loop:
- Trade on Aqua → Earn or stake AQUA → Gain perks → Trade more.
However, following the September 2025 presale, reports of the project disappearing with raised funds undercut confidence in the token’s reward model. What began as a potentially sustainable, value-capturing mechanism turned into an example of how tokenized incentive models can collapse without transparency.
Referral & Leaderboard System
Aqua offered a referral system that rewarded users with partial fee rebates or token bonuses. The leaderboard concept was mentioned early on but never fully implemented. The lack of public ranking or verifiable trade data limited its viral growth potential.
Business Model Pros
- Fee structure competitive within the Solana bot ecosystem.
- Built-in token model could have aligned user growth with platform revenue.
- Instant scalability through Telegram made onboarding frictionless.
Business Model Cons
- Heavy dependence on token speculation for long-term sustainability.
- No clear revenue transparency or fund management.
- Unverified backend custody of fees and token liquidity.
Sustainability Analysis
At launch, Aqua’s revenue engine relied on transaction fees and token demand. Without continuous trading volume and active backend operations, the model was unsustainable. The abrupt team disappearance post-launch demonstrated the fragility of this structure — especially when no audits, escrow systems, or DAO-style governance existed to safeguard funds.
Comparison with Alternatives
- Execution Speed and Latency
Aqua Bot’s transaction speed on Solana was genuinely impressive. Its private RPC routing allowed trades to confirm in roughly one second under normal conditions. Compared to others, Bloom Bot matched or slightly surpassed it with multi-chain optimization, while BONKbot was equally fast but less flexible. Maestro, operating on EVM, was slower due to higher block times. - Ease of Use
Aqua excelled in simplicity. The Telegram onboarding was frictionless—open, click Start, and you’re live. BONKbot had a similar flow but felt more crowded; Bloom Bot’s menus offered more power but also more complexity. Aqua’s minimalism made it appealing to beginners who wanted immediate access without technical barriers. - Automation Capabilities
This is where Aqua fell short. While it offered limit orders and retry protection, it lacked true automation—no event triggers, wallet mirroring, or strategy scripting. In contrast, Bloom Bot’s AFK trading and Maestro’s smart-trigger systems allowed fully unattended operation, which Aqua never achieved. - Feature Breadth
Aqua’s feature list focused tightly on execution—Buy, Sell, Limit, and MEV protection. Competitors like Bloom and Maestro layered on advanced tools like copy trading, social leaderboards, and smart alerts. Aqua’s limited scope made it fast but shallow, suited for execution, not strategy. - Security Transparency
Aqua’s backend was closed-source with no published audits or code transparency. Bloom Bot and Maestro, by comparison, disclosed smart contract details and operational architecture. BONKbot also earned higher user trust by demonstrating consistent uptime and open communication. Aqua’s security posture relied entirely on faith. - Cross-Chain Capability
Aqua remained strictly Solana-bound. Bloom Bot expanded to both Solana and EVM environments, bridging chains seamlessly. Maestro’s EVM focus gave it reach across multiple networks, while Photon operated natively on Solana but explored interoperability. Aqua’s single-chain stance limited its growth potential. - Community and Support
Early on, Aqua’s Telegram community was active, responsive, and well-managed. However, after the September 2025 presale, support vanished. Bloom Bot and Maestro maintain continuous moderation and developer interaction. BONKbot’s meme-driven community is smaller but loyal. Aqua’s community collapse directly impacted user confidence. - Fee Structure and Accessibility
Aqua charged lower trading fees than most competitors, often cited as one of the cheapest Solana bots at the time. Bloom Bot had flexible fees tied to features; Maestro introduced tiered pricing; BONKbot charged per transaction. Aqua’s affordability was its best competitive advantage—until trust issues overshadowed it. - Incentive and Token Model
Aqua was the only one that deeply tied its bot ecosystem to a native token, the AQUA token. The Liquidity Ladder was ambitious, meant to build a self-sustaining economy. But the alleged rug pull flipped it from innovation to liability. Other bots like Bloom and Maestro used reward points or non-custodial fee rebates instead—safer and more transparent. - Trust, Longevity, and Reputation
Ultimately, Aqua’s competitors outlived it because they balanced performance with credibility. Bloom Bot’s transparent upgrades, Maestro’s audits, and BONKbot’s consistent uptime all built long-term user confidence. Aqua, despite strong technology, collapsed under poor communication and zero transparency. In crypto, that’s the difference between a thriving tool and a ghost app.
Strengths & Weaknesses
Strengths
- Instant Setup – One-click Telegram onboarding with auto-generated wallet.
- Low Fees – Among the lowest Solana bot fee structures before shutdown.
- Strong Execution Speed – Private RPC routing allowed near-instant trades.
- Simple UX – Highly beginner-friendly for first-time traders.
- Innovative Token Model – Liquidity Ladder was a creative (if risky) launch design.
Weaknesses
- No Audits or Transparency – Critical security blind spot.
- Team Disappearance – Post-presale abandonment destroyed trust.
- Opaque Key Management – Users had to trust backend custody of wallets.
- Limited Automation – No trigger-based trading or advanced strategies.
- No EVM or Multi-Chain Capability – Missed opportunity for broader reach.
- Dependency on Telegram – No web or API alternatives for redundancy.
- Unverified Fee Handling – Users couldn’t confirm where fees were stored.
Dangers and Risks
- Custodial Exposure: Telegram-based wallets may expose keys.
- Liquidity Risk: Post-launch tokens could lose value quickly.
- Rug Potential: Centralized control without audits enables fund mismanagement.
- Reputation Fragility: One negative event can erase ecosystem confidence.
Conclusion and Rating
Aqua Bot began as a promising Solana trading companion — quick, sleek, and accessible. It lowered barriers for DEX trading, introduced creative tokenized incentives, and hinted at a more integrated trading experience.
But crypto isn’t kind to opacity.
When the AQUA Liquidity Ladder presale imploded in September 2025 and the team vanished, the project’s credibility went with it. What was once a clean, efficient trading interface became a cautionary case study on what happens when execution speed and flashy tokenomics outrun trust and transparency.
From a technical standpoint, Aqua’s architecture and feature set show strong engineering principles. Yet without audits, governance, or accountability, even the best-built systems can fail users.
Altie’s Verdict:
- For learning: Useful example of Solana bot structure.
- For trading: High risk — not recommended unless revived and audited.
- For the ecosystem: Proof that convenience without transparency is dangerous.
Ratings (Out of 10)
| Category | Score |
| Technical Architecture | 8.0 |
| Performance & Latency | 8.5 |
| Security & Transparency | 4.0 |
| Usability & Design | 9.0 |
| Business Model | 5.0 |
| Trust & Reliability | 2.0 |
| Overall | 6.1 / 10 |
Final Thoughts
Aqua Bot’s rise and fall mirror the volatility of the space it tried to dominate. It excelled at simplicity and speed but collapsed under the weight of its own ambition and poor transparency. The lesson? Even the fastest bot can’t outrun user distrust.
If a new team ever revives Aqua with audits, open-source backend, and verifiable wallet custody, it could reclaim its place as a serious contender in Solana’s trading bot ecosystem. Until then, it remains a fascinating but flawed chapter in the ongoing story of automated on-chain trading.