Algo Trading

Automate your strategies with precision.

My Strategies

Configure:

Algorithm Settings

Select Symbol & Timeframe

Pre-built Strategy Templates

Capital & Risk Settings

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:  { date, 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

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

# 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 = 1, candles[i]['open']

# 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({
                    '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

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

Visual Block Strategy Builder

Select Symbol & Timeframe

Starting Capital

Position Size

No symbol selected.

Block Library

Indicators

RSI Node

SMA Node

Values

Constant Value

Operators

Crosses Above

Crosses Below

OR Operator

Click templates to add nodes to the canvas. Drag from output ports (bottom) to input ports (top) to connect. Click a connection wire to delete it.

Backtest Results

Run a backtest to see results

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

Algo Trading

Visual Block Strategy Builder

My Strategies

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Configure:

Visual Block Strategy Builder

Block Library