Signal Best Practices

Creating Actionable Signals

A good trading signal is actionable. It tells the trader:

Where to enter
Where to place the stop loss
Where to take profits

Always include Entry, Stop Loss, and at least one Take Profit in your signals.

Signal Structure

Complete BUY Signal

{
  "points": [
    {
      "time": 1765540800,
      "type": "low",
      "price": 1.1725,
      "label": "BUY",
      "color": "#3b82f6",
      "shape": "arrowUp",
      "size": 2
    },
    {
      "time": 1765540800,
      "type": "low",
      "price": 1.1695,
      "label": "SL",
      "color": "#ef4444",
      "shape": "square",
      "size": 1
    },
    {
      "time": 1765540800,
      "type": "high",
      "price": 1.1755,
      "label": "TP1",
      "color": "#22c55e",
      "shape": "circle",
      "size": 1
    },
    {
      "time": 1765540800,
      "type": "high",
      "price": 1.1785,
      "label": "TP2",
      "color": "#22c55e",
      "shape": "circle",
      "size": 1
    },
    {
      "time": 1765540800,
      "type": "high",
      "price": 1.1815,
      "label": "TP3",
      "color": "#22c55e",
      "shape": "circle",
      "size": 1
    }
  ],
  "metadata": {
    "signal_type": "BUY",
    "entry": 1.1725,
    "sl": 1.1695,
    "tp1": 1.1755,
    "tp2": 1.1785,
    "tp3": 1.1815,
    "risk_pips": 30,
    "risk_reward": "1:3"
  }
}

Risk Management

Calculate Risk/Reward

def calculate_risk_reward(entry, stop_loss, take_profit):
    """Calculate risk/reward ratio."""
    risk = abs(entry - stop_loss)
    reward = abs(take_profit - entry)
    return reward / risk if risk > 0 else 0

# Example
entry = 1.1725
sl = 1.1695
tp = 1.1815

rr = calculate_risk_reward(entry, sl, tp)
print(f"Risk/Reward: 1:{rr:.1f}")  # Output: Risk/Reward: 1:3.0

Minimum R:R Requirements

Signal Quality	Minimum R:R
Standard	1:1
Good	1:2
Excellent	1:3+

Signals with R:R below 1:1 are generally not worth taking. Consider filtering these out.

Visual Hierarchy

Size Recommendations

Element	Size	Why
Entry	2	Most important - catches attention
Stop Loss	1	Secondary - always visible
Take Profits	1	Secondary - shows targets

Color Consistency

Always use consistent colors:

Element	Color	Hex
BUY Entry	Blue	`#3b82f6`
SELL Entry	Orange	`#f97316`
Stop Loss	Red	`#ef4444`
Take Profit	Green	`#22c55e`
Neutral/Info	Yellow	`#eab308`

Label Best Practices

DO

Use short labels: BUY, SELL, TP1, SL
Be consistent across signals
Include level numbers for TPs: TP1, TP2, TP3

DON’T

Avoid long labels: Buy Entry Here at Support
Don’t use special characters that may not render
Don’t mix naming conventions

Time Alignment

Always use the time value from the /bars endpoint. Never calculate timestamps manually.

# CORRECT
bars = get_bars("EURUSD", 60)
signal_time = bars.iloc[-1]["time"]  # Use time from API

# WRONG
signal_time = int(datetime.now().timestamp())  # May not align with bars

Multiple Signals on Same Bar

You can place multiple points on the same bar by using the same time value:

points = [
    {"time": bar_time, "type": "low", "price": entry, "label": "BUY", ...},
    {"time": bar_time, "type": "low", "price": sl, "label": "SL", ...},
    {"time": bar_time, "type": "high", "price": tp1, "label": "TP1", ...},
]

Signal Spacing

For visual clarity, add small offsets to prices:

def create_buy_signal(bar, entry_price, sl_price, tp_prices):
    """Create a BUY signal with proper spacing."""
    points = []

    # Entry - slightly below the low
    points.append({
        "time": int(bar["time"]),
        "type": "low",
        "price": entry_price,
        "label": "BUY",
        "color": "#3b82f6",
        "shape": "arrowUp",
        "size": 2
    })

    # SL - with offset below entry
    points.append({
        "time": int(bar["time"]),
        "type": "low",
        "price": sl_price,
        "label": "SL",
        "color": "#ef4444",
        "shape": "square",
        "size": 1
    })

    # TPs - with offsets above entry
    for i, tp in enumerate(tp_prices, 1):
        points.append({
            "time": int(bar["time"]),
            "type": "high",
            "price": tp,
            "label": f"TP{i}",
            "color": "#22c55e",
            "shape": "circle",
            "size": 1
        })

    return points

Metadata for Analytics

Include metadata to track signal performance:

metadata = {
    # Signal details
    "signal_type": "BUY",
    "entry_price": 1.1725,
    "stop_loss": 1.1695,
    "take_profits": [1.1755, 1.1785, 1.1815],

    # Risk metrics
    "risk_pips": 30,
    "reward_pips": 90,
    "risk_reward": "1:3",

    # Strategy info
    "strategy": "ICT Order Block",
    "timeframe": "H1",
    "confluence_factors": ["OB", "FVG", "HTF Trend"],

    # Confidence
    "confidence": "high",  # low, medium, high

    # Timestamp
    "generated_at": datetime.utcnow().isoformat()
}

Filter Low-Quality Signals

Before submitting, filter out weak signals:

def is_quality_signal(entry, sl, tp, min_rr=1.5):
    """Check if signal meets quality criteria."""
    rr = calculate_risk_reward(entry, sl, tp)
    return rr >= min_rr

# Filter signals
quality_signals = [
    s for s in all_signals
    if is_quality_signal(s["entry"], s["sl"], s["tp"])
]

Update Frequency

Timeframe	Recommended Update
M1	Every 1 minute
M5	Every 5 minutes
M15	Every 15 minutes
H1	Every hour
H4	Every 4 hours
D1	Once per day

Don’t update too frequently - it wastes API calls and can cause visual flickering.

Error Handling

Always handle API errors gracefully:

def safe_submit(points, max_retries=3):
    """Submit with retry logic."""
    for attempt in range(max_retries):
        try:
            return submit_indicator(points)
        except requests.exceptions.HTTPError as e:
            if e.response.status_code == 429:
                # Rate limited - wait and retry
                wait = 60 * (attempt + 1)
                print(f"Rate limited. Waiting {wait}s...")
                time.sleep(wait)
            elif e.response.status_code >= 500:
                # Server error - wait and retry
                time.sleep(5 * (attempt + 1))
            else:
                raise
    raise Exception("Max retries exceeded")

Checklist

Before deploying your indicator:

Verify time alignment

Signals appear on the correct candles

Check visual hierarchy

Entry is prominent, TPs/SL are secondary

Validate R:R ratios

All signals meet minimum R:R requirements

Test on multiple timeframes

Indicator works on H1, H4, D1

Add error handling

Gracefully handle API errors

Include metadata

Track signal performance

Next Steps

Continuous Integration

Set up automated signal updates

Python SDK

Complete Python reference

Get Started

MT4/MT5 Connection

Core Concepts

Guides

Examples

Support

Creating Actionable Signals

Signal Structure

Complete BUY Signal

Risk Management

Calculate Risk/Reward

Minimum R:R Requirements

Visual Hierarchy

Size Recommendations

Color Consistency

Label Best Practices

DO

DON’T

Time Alignment

Multiple Signals on Same Bar

Signal Spacing

Metadata for Analytics

Filter Low-Quality Signals

Update Frequency

Error Handling

Checklist

Next Steps

Continuous Integration

Python SDK

​Creating Actionable Signals

​Signal Structure

​Complete BUY Signal

​Risk Management

​Calculate Risk/Reward

​Minimum R:R Requirements

​Visual Hierarchy

​Size Recommendations

​Color Consistency

​Label Best Practices

​DO

​DON’T

​Time Alignment

​Multiple Signals on Same Bar

​Signal Spacing

​Metadata for Analytics

​Filter Low-Quality Signals

​Update Frequency

​Error Handling

​Checklist

​Next Steps

Continuous Integration

Python SDK

Creating Actionable Signals

Signal Structure

Complete BUY Signal

Risk Management

Calculate Risk/Reward

Minimum R:R Requirements

Visual Hierarchy

Size Recommendations

Color Consistency

Label Best Practices

DO

DON’T

Time Alignment

Multiple Signals on Same Bar

Signal Spacing

Metadata for Analytics

Filter Low-Quality Signals

Update Frequency

Error Handling

Checklist

Next Steps