UPSTOX · NEXUS

algorithmic trading core | realtime options engine

Open Source Multi-TF ATR Trail Dry-Run MACD EMA WS V3

View on GitHub Documentation

Core Config Strategies Workflow Risk Tech

System Core

Autonomous Upstox algo engine for NSE options. Real-time ticks → multi-frame candles → indicators → execution.

5 Timeframes

1m · 3m · 5m · 10m · 15m

Indicators

ATR, MACD, EMA, RSI, ADX, BB

Trailing Stop

ATR-based dynamic

Risk Controls

2% risk/trade

# clone & install
git clone https://github.com/Omega-Xi/Algo-Trading.git
cd Algo-Trading
pip install -r requirements.txt
python main.py --dry-run

SIMULATION ACTIVE | Kill switch ready

Engine Config

Parameter	Value	Impact
DRY_RUN	True	Paper trading
INTERVALS	1,3,5,10,15	Candle granularity
ATR_MULTIPLIER	1.5	Stop distance
RISK_PERCENT	2.0%	Risk per trade
R_TO_R_RATIO	2.0	Risk:Reward
ENTRY_COOLDOWN	30s	Anti-spam

STRATEGY_MAP = {
  "MACD EMA": macd_ema_strategy,      # ← ACTIVE
  "MACD RSI": macd_rsi_strategy,
  "Golden Strategy": golden_strategy,
  "Bollinger RSI": bollinger_rsi_mean_reversion
}

Current Active: MACD EMA — Trend-following with EMA smoothing

Strategy Vault

MACD EMA ★ ACTIVE

MACD+EMA200

MACD+RSI

MACD+ADX

VWAP+RSI

Bollinger+RSI

DI+/DI- ADX

Golden Strategy

MACD EMA Strategy — Combines MACD crossover with EMA smoothing for reduced noise and better trend identification.

Risk Engine

Position Sizing

Q = (M × R%) / (E - T)

margin * risk / (entry - trigger)

Trailing Stop

max(SL, high - ATR*1.5)

def update_stop_loss(self):
    new_sl = calc_trigger_price(atr, self.highest_price)
    self.trigger_price = max(self.trigger_price, new_sl)
    # modify Upstox order

Margin verified pre-entry: get_margin()

Trade Cycle

Auth & Preload

WS Connected

aggregate_candles()

MACD EMA Signal → ENTER

Monitor / Trail SL

Exit (SL/Target)

Exit Logic

Stop Loss: LTP ≤ trigger_price

Target Hit: LTP ≥ exit_price

Trailing: dynamic SL locks profits

if ltp <= self.trigger_price:
    self.exit_position()
    self.transcriber.record_exit(ltp, "STOPLOSS_HIT")

Data Pipeline

Historic 5d Tick→Candle Indicators

self.tick_buffer.append({"timestamp": ts, "price": ltp})
df.resample('1min').agg({'price': 'ohlc'})

ATR, MACD, EMA, RSI, ADX, Bollinger Bands injected into candle_df

Concurrency & Alerts

threading.Lock() atomic entry/exit

🔐 entry_lock → prevents overlapping signals
🔐 exit_lock → race‑free trailing

if self.exit_lock.acquire(blocking=False):
    try: check_exit_conditions()
    finally: self.exit_lock.release()

Alerts: WS connect/error, trade events, generic errors

Ready to deploy your own trading bot?

Star the repository, fork it, and start trading with our battle-tested MACD EMA strategy.

GitHub Repository Report Issues

⭐ Star on GitHub Fork & Contribute MIT License