Stochastic Oscillator: Overbought/Oversold via API

Section 01

What Is the Stochastic Oscillator?

The Stochastic Oscillator, developed by George Lane in the 1950s, compares a security's closing price to its price range over a specified period. The core idea is straightforward: in an uptrend, prices tend to close near the high of the range, and in a downtrend, they close near the low.

It produces two lines: %K (the fast line) and %D (the signal line, a moving average of %K). Both oscillate between 0 and 100, making it easy to identify extremes.

Overbought Zone

%K above 80 suggests the asset is overbought. This does not mean sell immediately -- it means upward momentum may be exhausting.

Oversold Zone

%K below 20 suggests the asset is oversold. Look for a %K/%D crossover before entering a long position.

Section 02

The Math Behind It

Understanding the formula helps you interpret the signals correctly:

Shell The Math Behind It

%K = 100 * (Close - Lowest Low) / (Highest High - Lowest Low)

%D = SMA(%K, 3)   # 3-period simple moving average of %K

Where:
  - Close      = most recent closing price
  - Lowest Low = lowest low over the lookback period (default: 14)
  - Highest High = highest high over the lookback period

When the close is near the top of the range, %K approaches 100. When it is near the bottom, %K approaches 0.

Section 03

Fetching Stochastic Data via API

The TickAtlas API returns both %K and %D in a single call — they are two keys, Stochastic_K and Stochastic_D, in the same response. Here is how to fetch the Stochastic Oscillator for EURUSD on the H1 timeframe:

Shell Fetching Stochastic Data via API

curl -H "X-API-Key: YOUR_API_KEY" \
  "https://tickatlas.com/v1/indicators?symbol=EURUSD&timeframe=H1"

Example response:

JSON Fetching Stochastic Data via API

{
  "success": true,
  "data": {
    "symbol": "EURUSD",
    "timeframe": "H1",
    "indicators": {
      "Stochastic_K": 23.45,
      "Stochastic_D": 28.12
    },
    "ohlcv": {
      "open": 1.0842,
      "high": 1.0859,
      "low": 1.0831,
      "close": 1.0836,
      "volume": 4521
    },
    "bid": 1.0836,
    "ask": 1.0837,
    "count": 2,
    "updated_at": 1787229614
  }
}

Section 04

Python: Detecting Crossovers

The most reliable Stochastic signal is the %K/%D crossover in an extreme zone. Here is a Python implementation that checks for this pattern:

Python Python: Detecting Crossovers

import requests

API_KEY = "YOUR_API_KEY"
BASE_URL = "https://tickatlas.com/v1"

def get_stochastic(symbol: str, timeframe: str) -> tuple[float, float]:
    """Fetch current %K and %D in one call."""
    resp = requests.get(
        f"{BASE_URL}/indicators",
        headers={"X-API-Key": API_KEY},
        params={
            "symbol": symbol,
            "timeframe": timeframe,
        },
    )
    resp.raise_for_status()
    ind = resp.json()["data"]["indicators"]
    return ind["Stochastic_K"], ind["Stochastic_D"]

def check_stochastic_signal(symbol: str, timeframe: str) -> str:
    """Detect overbought/oversold crossover signals."""
    values = get_stochastic(symbol, timeframe)
    k, d = values

    if k < 20 and k > d:
        return "BULLISH_CROSSOVER_OVERSOLD"
    elif k > 80 and k < d:
        return "BEARISH_CROSSOVER_OVERBOUGHT"
    elif k < 20:
        return "OVERSOLD"
    elif k > 80:
        return "OVERBOUGHT"
    else:
        return "NEUTRAL"

# Check multiple pairs
pairs = ["EURUSD", "GBPUSD", "USDJPY", "XAUUSD"]
for pair in pairs:
    signal = check_stochastic_signal(pair, "H1")
    print(f"{pair}: {signal}")

Section 05

Combining Stochastic with Trend Filters

The Stochastic works best when combined with a trend indicator. In a strong uptrend, oversold readings are high-probability buy signals. In a downtrend, overbought readings are better short entries.

Python Combining Stochastic with Trend Filters

def confluence_check(symbol: str) -> dict:
    """Combine Stochastic with EMA trend filter."""
    # Fetch both indicators in one multi call
    resp = requests.get(
        f"{BASE_URL}/indicators",
        headers={"X-API-Key": API_KEY},
        params={
            "symbol": symbol,
            "timeframe": "H4",
        },
    )
    data = resp.json()["data"]

    ind = data["indicators"]
    stoch_k = ind["Stochastic_K"]
    stoch_d = ind["Stochastic_D"]
    ema_50 = ind["EMA_50"]
    close = data["ohlcv"]["close"]

    trend = "UP" if close > ema_50 else "DOWN"

    signal = None
    if trend == "UP" and stoch_k < 20 and stoch_k > stoch_d:
        signal = "BUY"
    elif trend == "DOWN" and stoch_k > 80 and stoch_k < stoch_d:
        signal = "SELL"

    return {
        "symbol": symbol,
        "trend": trend,
        "stochastic_k": stoch_k,
        "stochastic_d": stoch_d,
        "signal": signal,
    }

Section 06

Common Mistakes to Avoid

Selling just because %K hit 80

In strong trends, the Stochastic can stay overbought for extended periods. Wait for the crossover, not just the zone entry.

Using it on very low timeframes without filtering

On M1 or M5, the Stochastic produces frequent whipsaw signals. Always pair with a higher-timeframe trend filter.

Ignoring divergence

When price makes a new high but the Stochastic makes a lower high, momentum is fading. This divergence is often more valuable than simple overbought/oversold readings.

Section 07

Next Steps

CCI Mean Reversion Strategy

Another oscillator approach to timing entries

API Quick Start

Get set up in 5 minutes

Stochastic Oscillator: Overbought/Oversold Detection via API