Field	Accepted column names	Required?
`time`	`time, timestamp, t, date, datetime`	✅ Yes
`open`	`open, o`	✅ Yes
`high`	`high, h`	✅ Yes
`low`	`low, l`	✅ Yes
`close`	`close, c`	✅ Yes
`volume`	`volume, vol, v`	Optional

Algo Trading

Automate your strategies with precision.

My Strategies

Configure:

Algorithm Settings

Select Symbol & Timeframe

Pre-built Strategy Templates

Capital & Risk Settings

Currency

Starting Capital

Position Size / Trade

Commission / Trade

Slippage / Trade

Trade Direction

Strategy Parameters

Python Backtest Script

# ═══════════════════════════════════════════════════════════
# BACKTESTX  —  Custom Python Strategy Editor
# ═══════════════════════════════════════════════════════════
# `candles`  →  auto-injected list of dicts (your timeframe)
#   keys: time (unix ms), open, high, low, close, volume
#
# MUST return:  json.dumps(trades)
# Each trade:  { entryDate, exitDate, action, entry, exit, result (+/-X.XX%) }
#
# Built-ins: json, math, datetime, statistics
# ═══════════════════════════════════════════════════════════
import json, math, datetime

# ── Helper functions ────────────────────────────────────────
def sma(data, period):
    return [sum(data[max(0,i-period+1):i+1])/min(i+1,period)
            for i in range(len(data))]

def rsi(closes, period=14):
    result, gains, losses = [], [], []
    for i in range(1, len(closes)):
        d = closes[i] - closes[i-1]
        gains.append(max(d,0)); losses.append(max(-d,0))
        if i < period:
            result.append(50); continue
        ag = sum(gains[-period:])/period
        al = sum(losses[-period:])/period
        rs = ag/al if al > 0 else 100
        result.append(100 - 100/(1+rs))
    return [50] + result

# ── Strategy ────────────────────────────────────────────────
def run_strategy():
    trades = []
    closes = [c['close'] for c in candles]
    fast   = sma(closes, 20)
    slow   = sma(closes, 50)
    rsi14  = rsi(closes, 14)

position, entry = 0, 0.0
    entry_time = ""

for i in range(50, len(candles)):
        dt = datetime.datetime.fromtimestamp(
            candles[i]['time']/1000).strftime('%Y-%m-%d %H:%M')

# BUY: fast x slow + RSI not overbought
        if fast[i-1] <= slow[i-1] and fast[i] > slow[i] and rsi14[i] < 70:
            if position == 0:
                position, entry, entry_time = 1, candles[i]['open'], dt

# SELL: fast x slow OR RSI overbought
        elif (fast[i-1] >= slow[i-1] and fast[i] < slow[i]) or rsi14[i] > 75:
            if position == 1:
                pct = (candles[i]['open'] - entry) / entry * 100
                trades.append({
                    'entryDate': entry_time,
                    'exitDate': dt,
                    'date': dt,
                    'action': 'CLOSE LONG',
                    'entry': round(entry, 4),
                    'exit':  round(candles[i]['open'], 4),
                    'result': f"{'+' if pct>=0 else ''}{pct:.2f}%"
                })
                position = 0

return json.dumps(trades)

run_strategy()

Strategy Notes

📅 Historical Data Selection

Select a symbol first

Hold-select multiple years — data will be merged chronologically.

Upload Custom Candle Data (CSV or JSON — overrides year selection)

Drag & drop or browse a file

CSV: time,open,high,low,close,volume | JSON: [{time,open,high,low,close,volume}]

No file loaded — using IndexedDB years above

Backtest Results

Run a backtest to see results

#	Entry Time	Exit Time	Action	Entry	Exit	Result %	P&L ($)	Equity ($)
Run a backtest to see trade results

Strategy Guide & Data Docs

1. The candles array

2. Required Output

3. Trade Dictionary Keys

4. Minimal Skeleton

Uploading a CSV File

Valid CSV Example

Uploading a JSON File

Common Pitfalls

Algo Trading

My Strategies

No Strategies Yet

Configure:

1. The `candles` array