The most valuable trading signals are the ones tailored to your specific strategy, timeframe, and risk tolerance. Generic signals serve generic strategies-and generic strategies produce generic results. But creating custom AI trading signals has traditionally required programming skills that most traders lack.
Not anymore. The AI custom trading signal builder landscape has transformed. No-code platforms now let traders design sophisticated multi-factor signals combining technical analysis, on-chain data, sentiment metrics, and AI interpretation-all without writing a single line of code.
This guide walks you through building custom AI trading crypto signals from scratch. You'll learn signal design principles, how to combine data sources effectively, which no-code tools enable custom signal creation, and how to test and refine your signals for real-world trading. By the end, you'll have the knowledge to create personalized signal systems that match your exact trading approach.
Before investing time in custom signal building, understand why it matters.
Generic signals serve the average trader-but you're not average:
Your strategy is unique:
- Specific timeframe preferences
- Particular risk tolerance
- Defined asset focus
- Personal schedule constraints
Generic signals ignore this:
- One-size-fits-all conditions
- No personalization to your edge
- Optimized for no one specifically
- Crowded when popular
| Generic Signals |
Custom Signals |
| Same for everyone |
Tailored to your strategy |
| Crowded trades |
Unique to your approach |
| May conflict with your style |
Aligned with your preferences |
| No control over parameters |
Full parameter customization |
| Dependent on provider |
Self-maintained and understood |
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Custom signals create edge through: Specificity: Signals precisely matching your entry criteria
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Combination: Multi-factor signals only you combine that way
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Timing: Alerts optimized for your trading hours
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Context: Signals that understand your specific focus
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Evolution: Ability to modify as markets and strategies change
Good candidates:
- Traders with defined strategies needing alert automation
- Those frustrated by generic signal quality
- Anyone wanting to understand signals deeply
- Traders with unique market perspectives to encode
Not ideal candidates:
- Complete beginners (learn basics first)
- Those seeking "done for you" solutions
- Traders without clear strategy criteria
Effective signals share common design principles.
Every trading signal has core components:
- Trigger Conditions:
What must happen for signal to fire?
- Price crosses above moving average
- Volume exceeds threshold
- Multiple conditions combine
- Asset Scope:
Which assets does the signal monitor?
- Single asset focus
- Category (e.g., large caps)
- Full market scan
- Timeframe:
What data timeframe?
- Intraday (1m, 5m, 15m, 1h)
- Daily
- Weekly
- Multiple timeframes combined
- Direction:
What does the signal indicate?
- Long opportunity
- Short opportunity
- Direction-agnostic (volatility)
- Confidence/Strength:
How strong is the signal?
- Context:
Supporting information?
- Market conditions
- Risk factors
- Invalidation levels
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Good signals are: Clear: Unambiguous conditions that either meet or don't
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Timely: Fire with enough lead time to act
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Actionable: Include enough information to make decisions
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Consistent: Same conditions produce same signals
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Valuable: Actually correlate with profitable opportunities
| Strategy |
Signal Focus |
| Trend Following |
Trend initiation, trend continuation |
| Mean Reversion |
Oversold/overbought extremes |
| Breakout |
Support/resistance breaches |
| Momentum |
Acceleration, relative strength |
| Event-Driven |
News, on-chain events |
| Arbitrage |
Price discrepancies |
- The most powerful signals combine multiple factors: Example multi-factor signal:
- Factor 1: Price above 50-day MA (trend confirmation)
- Factor 2: RSI below 40 (pullback condition)
- Factor 3: Volume spike (interest confirmation)
- Factor 4: Positive funding rate (market positioning)
- Combination: All four factors present = signal
Single factors are noisy. Combined factors filter noise.
These platforms enable custom signal creation without programming.
Signal capabilities:
- Custom indicators via visual builder
- Alert conditions on any indicator
- Pine Script for advanced users (low-code)
- Community scripts adaptable
Strengths:
- Most comprehensive charting
- Huge indicator library
- Excellent alert system
- Large community
Limitations:
- Limited on-chain data
- Basic AI capabilities
- Alert quantity limited on free tier
Best for: Technical analysis-based signals
Signal capabilities:
- Strategy designer (visual)
- Multiple signal sources
- AI-powered templates
- Backtesting built-in
Strengths:
- Trading bot integration
- Multiple exchange support
- Signal marketplace
Limitations:
- Can be complex
- Subscription required for full features
- Limited on-chain integration
Best for: Automated trading execution with signals
Signal capabilities:
- TradingView signal integration
- Custom condition combinations
- Smart trading features
- Multi-exchange alerts
Strengths:
Limitations:
Signal capabilities:
- Connect any data source with webhooks
- Combine sources creatively
- Custom logic flows
- Unlimited integrations
Strengths:
- Maximum flexibility
- Connect anything to anything
- No-code logic building
- Many integrations
Limitations:
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Requires understanding of data flows
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Not crypto-specific
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May need multiple tool subscriptions
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Best for: Advanced users wanting maximum customization
Signal capabilities:
- Multi-factor alert builder
- On-chain + technical combination
- AI signal interpretation
- Integrated with trading tools
Strengths:
- Purpose-built for crypto
- Combines multiple data types
- AI enhancement of signals
- Trading workflow integration
Limitations:
→ Build Custom AI Signals With Thrive
The power of custom signals lies in multi-source combination.
Technical (Price-Based):
- Moving averages
- Oscillators (RSI, MACD)
- Volume indicators
- Chart patterns
- Support/resistance levels
On-Chain:
Derivatives:
Sentiment:
- Social volume
- Fear and greed index
- News sentiment
- Search trends
Fundamental:
- Protocol revenue
- TVL changes
- Developer activity
- Tokenomics events
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Confirmation approach: Multiple sources confirming same thesis.
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Example: Bullish signal when:
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Technical: Price above MA + RSI turning up
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On-chain: Exchange outflows increasing
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Sentiment: Fear index below 30
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All three = higher confidence long signal
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Filtration approach: One source generates candidates, another filters.
Example:
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Technical generates potential entries
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On-chain filters out those with whale selling
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Sentiment filters out extreme greed conditions
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Weighting approach: Score each factor, combine scores.
Example:
- Technical score: 7/10
- On-chain score: 8/10
- Sentiment score: 6/10
- Combined: (7+8+6)/3 = 7/10 signal strength
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Direct integration: Platform accesses multiple data sources natively.
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Simplest approach
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Limited to platform capabilities
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Webhook integration: External data feeds into signal platform.
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More flexible
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Requires setup
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API aggregation: Collect data via APIs, process centrally.
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Maximum flexibility
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Most technical
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Manual combination: Check multiple sources manually before acting on signals.
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Most accessible
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Slowest, most labor-intensive
Let's build a practical custom signal together.
Before building, clarify what you're looking for:
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Strategy: Trend pullback entries on major cryptocurrencies
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Thesis: In established uptrends, pullbacks to key levels offer favorable entry with defined risk.
Entry criteria:
- Uptrend established (price above 50-day MA)
- Pullback occurring (RSI below 40)
- Volume confirmation (increasing on pullback)
- Support level nearby (for stop placement)
For this example: TradingView + Thrive
- TradingView for technical conditions
- Thrive for AI interpretation and on-chain context
Condition 1: Trend confirmation
- Price above 50-day Simple Moving Average
- MA slope positive (trending up)
Condition 2: Pullback
- RSI(14) crosses below 40
- OR price touches 20-day MA
Condition 3: Volume
- Current volume above 50-period average volume
Combined condition:
(Price > 50MA) AND (MA slope positive) AND (RSI < 40 OR Price near 20MA) AND (Volume > Average)
In TradingView:
- Open chart with conditions applied
- Right-click → Add Alert
- Set condition: "Custom script condition met"
- Configure notification method
- Set alert name: "Trend Pullback Entry"
Use Thrive for additional context:
- Check on-chain flows (are whales accumulating?)
- Review sentiment (is fear elevated?)
- Verify no major resistance overhead
When signal fires, capture:
- Asset name
- Current price
- Signal conditions met
- Suggested stop level
- On-chain context
- Sentiment context
- Paper trade the signal for 2-4 weeks
- Track every signal occurrence
- Measure would-be results
- Refine before committing capital
Signals mean nothing without validation.
- What backtesting does: Tests signal performance on historical data.
Key metrics:
- Total signals generated
- Win rate
- Average return per signal
- Maximum drawdown
- Profit factor
- Sharpe ratio
Platforms with backtesting:
- TradingView (Pine Script)
- Cryptohopper
- Specialized backtesting tools
- Overfitting: Optimizing parameters to fit past data perfectly-fails on new data.
Mitigation: Keep parameters simple. Test on out-of-sample data.
Look-ahead bias:
Using information that wouldn't have been available at signal time.
Mitigation: Ensure all data inputs were available when signal would fire.
- Survivorship bias: Only testing on assets that still exist (survivors).
Mitigation: Include delisted assets in historical tests.
- Transaction costs: Ignoring fees, slippage, spread.
Mitigation: Include realistic cost assumptions.
After backtesting looks promising:
- Paper trade live:
- Follow signals in real-time
- Track without real money
- Minimum 30 signals
- Compare to backtest:
- Similar performance?
- Unexpected behaviors?
- Execution challenges?
- Refine based on findings:
- Adjust parameters if needed
- Address practical issues
- Improve alert content
Once live, track ongoing performance:
| Metric |
How to Track |
| Signal frequency |
Count per period |
| Win rate |
Wins / Total signals |
| Average R |
(Wins × Avg Win R) - (Losses × Avg Loss R) |
| Expectancy |
Win% × Avg Win - Loss% × Avg Loss |
| Best performers |
Which signals worked best? |
| Worst performers |
Which signals failed? |
Use this data to continuously improve signals.
Refinement transforms good signals into great ones.
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Test how parameter changes affect results: Example: RSI threshold
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Test RSI < 35, 40, 45, 50
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Compare signal frequency and quality
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Find optimal threshold
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Example: Moving average length
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Test 20, 50, 100, 200-day
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Compare trend detection quality
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Balance responsiveness and reliability
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One parameter at a time:
Change single variables to isolate effects.
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Test on multiple periods:
Optimal parameters should work across different market conditions.
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Prefer robustness over optimization:
Parameters that work "pretty well" across many conditions beat parameters that work "perfectly" in one condition.
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Avoid curve fitting:
If adding complexity doesn't significantly improve results, keep it simple.
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Reserve test data:
Optimize on one dataset, validate on another (out-of-sample).
- Advanced signals adjust parameters based on conditions: Volatility-adjusted RSI:
- High volatility → Wider RSI thresholds (30/70 instead of 40/60)
- Low volatility → Tighter thresholds
Volume-adjusted position:
- Higher volume on signal → Stronger signal
- Lower volume → Weaker signal, smaller position
Market regime adjustment:
- Bull market → More aggressive parameters
- Bear market → More conservative parameters
Triggers for review:
- Performance deteriorating over 30+ signals
- Market regime change evident
- New data sources available
- Strategy evolution
Frequency:
- Minor review: Monthly
- Major review: Quarterly
- Full rebuild: Annually or when strategy changes
Signals need delivery systems to be useful.
| Method |
Speed |
Reliability |
Best For |
| Push notification |
Fast |
High |
Active trading |
| Email |
Medium |
High |
Less time-sensitive |
| SMS |
Fast |
Very high |
Critical alerts |
| Telegram/Discord |
Fast |
Medium |
Community/bots |
| Webhook |
Instant |
High |
Automation |
Minimum alert content:
- Asset name
- Signal type (buy/sell/watch)
- Current price
- Time
Better alert content:
- All minimum items
- Condition summary (what triggered)
- Key levels (stop, target)
- Confidence indicator
- Context notes
Example alert:
🟢 BTC PULLBACK ENTRY
Price: $67,450
- **Signal:** Trend pullback (RSI 38, at 20-day MA support)
Stop: $65,800 (-2.4%)
Target: $72,000 (+6.7%)
Confidence: 7/10
- **Context:** Exchange outflows positive, Fear Index 35
Too many alerts = alert fatigue = missed signals.
Filtering strategies:
- Confidence thresholds (only alert if score > 7)
- Time-based (no alerts outside trading hours)
- Asset-based (only your focus assets)
- Rate-limiting (maximum 3 alerts per hour)
- Alert received
- Verification (30 seconds):
- Is alert legitimate (not error)?
- Are current conditions still valid?
- Any invalidating factors?
- Context check (
1-2 minutes):
- Check additional data sources
- Confirm thesis still holds
- Identify any concerns
- Decision:
- Take trade
- Pass on this signal
- Wait for better entry
- Execution:
- Entry order
- Stop loss set
- Position size appropriate
- Logging:
- Record signal and decision
- Note any deviations
- Track for future analysis
Avoid these pitfalls.
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The error: Adding more conditions thinking they'll improve accuracy.
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Reality: More conditions = fewer signals = less opportunity and statistical significance.
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Solution: Start simple. Add complexity only when data shows improvement.
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The error: Tweaking parameters until backtest looks perfect.
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Reality: Over-optimized signals fail on new data.
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Solution: Use out-of-sample testing. Prefer robust over optimal.
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The error: Signal looks great without accounting for fees and slippage.
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Reality: Frequent signals with small edges become losers after costs.
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Solution: Include realistic costs in all testing.
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The error: Building entry signals without corresponding exit signals.
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Reality: Entries without exits are incomplete systems.
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Solution: Design exit conditions with equal rigor.
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The error: Going live after limited backtesting or no paper trading.
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Reality: Live markets reveal problems backtests miss.
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Solution: Minimum 30 paper-traded signals before real capital.
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The error: Setting alerts for every signal variation.
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Reality: Alert fatigue leads to ignoring important signals.
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Solution: Filter ruthlessly. Fewer, better alerts beat many mediocre ones.
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The error: Ditching signals after a few losses.
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Reality: All signals have losing periods; abandoning during drawdowns ensures only losses are captured.
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Solution: Commit to signal systems for meaningful sample sizes (50+ signals).
Once basics are mastered, explore advanced approaches.
Instead of binary signals, score based on confluence:
Scoring model:
- Factor 1 present: +2 points
- Factor 2 present: +2 points
- Factor 3 present: +2 points
- Factor 4 present: +1 point
- Factor 5 present: +1 point
- Maximum: 8 points
Signal tiers:
- 7-8 points: Strong signal (full position)
- 5-6 points: Moderate signal (half position)
- 3-4 points: Weak signal (watch only)
- 0-2 points: No signal
- Conditions that adapt to market conditions: Volatility-adjusted:
- High volatility: Widen thresholds
- Low volatility: Tighten thresholds
Trend-adjusted:
- Strong trend: Favor continuation signals
- Ranging: Favor reversal signals
Correlation-adjusted:
- High BTC correlation: Filter by BTC signals first
- Low correlation: Asset-specific signals only
- Signals based on relationships between assets: Ratio trading:
- ETH/BTC ratio at support = long ETH relative signal
- Alt/BTC ratios for relative value
Correlation breakdown:
- Normally correlated assets diverge = potential opportunity
Sector rotation:
- Strength rotating into sector = sector-wide signals
Parameter optimization:
ML-guided parameter search (with overfitting safeguards).
- Diversify across signal types: Signal portfolio concept:
- Trend-following signals
- Mean-reversion signals
- Event-driven signals
- Uncorrelated with each other
- Combined for smoother equity curve
No programming is required with modern no-code platforms. Basic understanding of trading concepts (moving averages, RSI, support/resistance) is necessary. Familiarity with the platform interface takes a few hours to develop. The most important requirement is a clear trading strategy to encode into signals-technical knowledge of building comes easier than trading knowledge.
Start with one core signal that matches your primary strategy. Perfect it before adding more. Most traders do better with 2-3 high-quality signals than a dozen mediocre ones. Each signal should serve a specific purpose and not overlap significantly with others.
Thirty signals is the bare minimum for basic assessment; fifty is better for initial conclusions. One hundred or more signals are needed for statistically significant conclusions. Be cautious about optimizing or abandoning signals based on fewer than thirty occurrences-that's not enough data.
Yes, many platforms allow signal sharing or selling. TradingView has a marketplace for indicators. Signal services can be built on custom alerts. However, be aware that widely-shared signals lose edge as more traders use them. Consider whether sharing aligns with your goals.
Signs of over-complexity: signal fires rarely (fewer than twice weekly for active strategies), requires many conditions all present simultaneously, backtesting shows perfect historical results (probably overfit), or you can't explain why each condition matters. Simplify until signal fires regularly enough to test meaningfully.
Custom signals complement rather than replace professional services. Professional services offer research capacity you may lack. Custom signals address your specific strategy needs. The best approach often combines: professional signals for discovery/awareness plus custom signals for your specific entry criteria.
Building custom AI trading signals without code is now accessible through platforms like TradingView, Cryptohopper, 3Commas, and Thrive. Custom signals beat generic ones by tailoring conditions to your specific strategy, timeframe, and risk tolerance rather than serving a generic average trader. Effective signal design requires clear trigger conditions, defined asset scope, appropriate timeframe, directional indication, and confidence scoring. Multi-factor signals combining technical, on-chain, derivatives, and sentiment data provide stronger, less noisy signals than single-factor approaches. The build process involves defining your strategy, choosing a platform, constructing technical conditions, creating alerts, adding context layers, and testing extensively. Validation through backtesting and paper trading is essential-minimum 30 signals before live trading. Parameter optimization should favor robustness over perfection, testing across multiple market conditions. Alert systems must deliver timely, content-rich notifications without causing alert fatigue. Common mistakes include over-complication, overfitting to historical data, ignoring transaction costs, and insufficient testing. Advanced techniques include confluence scoring, dynamic condition adjustment, cross-asset signals, and signal portfolio diversification.
Thrive makes custom signal building accessible and powerful:
✅ Multi-Factor Signal Builder - Combine technical, on-chain, and sentiment conditions
✅ AI Signal Enhancement - Machine learning interpretation of your custom signals
✅ No-Code Interface - Visual signal creation without programming
✅ Backtesting Integration - Test signals before going live
✅ Smart Alert Delivery - Right signals, right time, right format
✅ Performance Tracking - Monitor how your signals perform over time
Create signals that match YOUR strategy, not someone else's.
→ Build Custom AI Signals With Thrive
