Two Philosophies of Price
Every trading strategy is fundamentally based on one of two beliefs about price behavior: either prices tend to continue in the current direction (trend following), or prices tend to return to a central value (mean reversion). The market alternates between regimes where each approach works.
Trend Following
Buy when price is going up, sell when it is going down. Ride the trend until it reverses.
Indicators: MACD, EMA crossovers, ADX, Parabolic SAR
Win rate: 30-40% with large winners
Mean Reversion
Buy when price is "too low," sell when it is "too high." Bet on return to average.
Indicators: RSI, CCI, Bollinger Bands, Stochastic
Win rate: 60-70% with smaller winners
Detecting the Current Regime
ADX is the most reliable regime detector. High ADX (above 25) indicates a trending market; low ADX (below 20) indicates a ranging market.
import requests
API_KEY = "YOUR_API_KEY"
BASE_URL = "https://tickatlas.com/v1"
def detect_regime(symbol: str, timeframe: str = "D1") -> dict:
"""Detect whether market is trending or ranging."""
# Fetch ADX
adx_resp = requests.get(
f"{BASE_URL}/indicator",
headers={"X-API-Key": API_KEY},
params={"symbol": symbol, "indicator": "ADX", "timeframe": timeframe},
)
adx = adx_resp.json()["data"]["value"]
# Fetch the Bollinger Bands for confirmation (one call, three keys)
bb_resp = requests.get(
f"{BASE_URL}/indicators",
headers={"X-API-Key": API_KEY},
params={"symbol": symbol, "timeframe": timeframe},
)
bb = bb_resp.json()["data"]["indicators"]
bb_width = (bb["BB_upper"] - bb["BB_lower"]) / bb["BB_middle"] * 100
if adx > 30:
regime = "STRONG_TREND"
strategy = "trend_following"
elif adx > 25:
regime = "TRENDING"
strategy = "trend_following"
elif adx < 15:
regime = "RANGE_BOUND"
strategy = "mean_reversion"
else:
regime = "TRANSITIONING"
strategy = "wait"
return {
"symbol": symbol,
"adx": round(adx, 1),
"bb_width_pct": round(bb_width, 2),
"regime": regime,
"recommended_strategy": strategy,
}
# Check multiple symbols
for sym in ["EURUSD", "GBPUSD", "XAUUSD", "BTCUSD"]:
result = detect_regime(sym)
print(f"{sym}: {result['regime']} (ADX={result['adx']}) "
f"-> {result['recommended_strategy']}")Trend Following Implementation
def trend_following_signal(symbol: str, timeframe: str) -> dict:
"""EMA crossover trend following strategy."""
ema_fast = requests.get(
f"{BASE_URL}/indicator", headers={"X-API-Key": API_KEY},
params={"symbol": symbol, "indicator": "EMA_20", "timeframe": timeframe},
).json()["data"]["value"]
ema_slow = requests.get(
f"{BASE_URL}/indicator", headers={"X-API-Key": API_KEY},
params={"symbol": symbol, "indicator": "EMA_50", "timeframe": timeframe},
).json()["data"]["value"]
macd_hist = requests.get(
f"{BASE_URL}/indicator", headers={"X-API-Key": API_KEY},
params={"symbol": symbol, "indicator": "MACD_hist", "timeframe": timeframe},
).json()["data"]["value"]
signal = None
if ema_fast > ema_slow and macd_hist > 0:
signal = "LONG"
elif ema_fast < ema_slow and macd_hist < 0:
signal = "SHORT"
return {"symbol": symbol, "signal": signal, "type": "trend_following"}Mean Reversion Implementation
def mean_reversion_signal(symbol: str, timeframe: str) -> dict:
"""RSI + Bollinger Band mean reversion strategy."""
rsi = requests.get(
f"{BASE_URL}/indicator", headers={"X-API-Key": API_KEY},
params={"symbol": symbol, "indicator": "RSI_14", "timeframe": timeframe},
).json()["data"]["value"]
bb_resp = requests.get(
f"{BASE_URL}/indicators", headers={"X-API-Key": API_KEY},
params={"symbol": symbol, "timeframe": timeframe},
).json()["data"]
close = bb_resp["ohlcv"]["close"]
lower = bb_resp["indicators"]["BB_lower"]
upper = bb_resp["indicators"]["BB_upper"]
signal = None
if rsi < 30 and close <= lower:
signal = "LONG" # Oversold + at lower band
elif rsi > 70 and close >= upper:
signal = "SHORT" # Overbought + at upper band
return {"symbol": symbol, "signal": signal, "type": "mean_reversion"}The Adaptive Approach
def adaptive_strategy(symbol: str, timeframe: str) -> dict:
"""Switch strategy based on current market regime."""
regime = detect_regime(symbol)
if regime["recommended_strategy"] == "trend_following":
return trend_following_signal(symbol, timeframe)
elif regime["recommended_strategy"] == "mean_reversion":
return mean_reversion_signal(symbol, timeframe)
else:
return {"symbol": symbol, "signal": None, "type": "waiting"}
# Scan and adapt
for sym in ["EURUSD", "GBPUSD", "XAUUSD"]:
result = adaptive_strategy(sym, "H4")
if result["signal"]:
print(f"{sym}: {result['signal']} ({result['type']})")
else:
print(f"{sym}: No signal")Which to Code First?
Start with mean reversion if you prefer higher win rates
Mean reversion strategies win more often (60-70%) but each win is smaller. This is psychologically easier for most traders and simpler to code.
Start with trend following if you can tolerate frequent losses
Trend following wins less often (30-40%) but the winners are large enough to compensate. You need the discipline to take many small losses before the big win.
Build both and let the regime decide
The best approach is to implement both and use ADX or Bollinger Band width to automatically switch between them. This is more complex but far more robust.
Run this against live data.
Every account starts pay-as-you-go with $2.50 of credit and no card. Paste the key into the samples above and the requests work unchanged.