freqtrade-strategy-dev

# Freqtrade Strategy Development Build profitable trading strategies with disciplined iteration, tight risk management, and data-driven entry/exit rules. Assumes Freqtrade is running via Docker (`docker-compose`). ## Strategy Anatomy Every Freqtrade strategy requires three methods: - **`populate_indicators(dataframe, metadata)`** — Add technical indicators (RSI, MACD, Bollinger Bands, etc.) to the dataframe - **`populate_entry_trend(dataframe, metadata)`** — Define buy signal logic; set `enter_long = 1` when conditions met - **`populate_exit_trend(dataframe, metadata)`** — Define sell signal logic; set `exit_long = 1` when conditions met (optional if using ROI/stop-loss) ## Key Config Parameters ```python stoploss = -0.03 # 3% max loss per trade trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 minimal_roi = { "0": 0.04, # 4% profit target immediately "30": 0.02, # 2% after 30 candles "60": 0.01, # 1% after 60 candles } timeframe = "5m" # or "15m", "1h", etc. stake_currency = "USDT" dry_run = True # Always backtest/dry-run first ``` ## Proven Entry Pattern ```python stoploss = -0.03 trailing_stop = True trailing_stop_positive = 0.01 trailing_stop_positive_offset = 0.02 minimal_roi = {"0": 0.04, "30": 0.02, "60": 0.01} # In populate_indicators: calculate RSI, CCI, Bollinger Bands, EMA, Volume SMA # In populate_entry_trend: only buy when ALL conditions met conditions = [ (dataframe['rsi'] < 30), # Oversold (dataframe['cci'] < -100), # Momentum confirmation (dataframe['close'] < dataframe['bb_lowerband']), # Price near lower band (dataframe['volume'] > dataframe['volume_sma']), # Volume confirms (dataframe['bullish_candle']), # Pattern confirmation ] dataframe.loc[reduce(lambda x, y: x & y, conditions), 'enter_long'] = 1 ``` ## Key Lessons Learned 1. **Tight stops save accounts** — 3% max loss beats 5%, 7%, or 8% every time 2. **Quality over quantity** — 25 selective trades outperform 308 mediocre ones 3. **Win rate alone is meaningless** — 63% win rate unprofitable if avg loss is 5x avg gain 4. **Selectivity is survival** — RSI(30) + CCI(-100) dual filters dramatically reduce noise 5. **Test in bear markets** — If strategy survives a crash, it works everywhere 6. **Volume confirms conviction** — Entries without above-average volume fail more often ## Useful Indicators - **RSI (14)** — Momentum; < 30 = oversold, > 70 = overbought - **CCI** — Commodity Channel Index; momentum confirmation; < -100 = deep oversold - **MACD** — Trend following; watch for crossovers - **Bollinger Bands** — Volatility; price near lower band = potential reversal - **EMA** — Trend filter; price above EMA = uptrend - **MFI** — Money Flow Index; volume-weighted momentum ## Iteration Workflow 1. Write baseline strategy with core entry/exit logic 2. Backtest on 90–120 days of historical data 3. Analyze exit reasons: are you exiting winners or losers too fast? 4. Tighten ONE parameter at a time (e.g., RSI threshold) 5. Backtest same period, compare vs. baseline 6. If better → keep; if worse → revert 7. Test different market conditions (Bull, bear, sideways) 8. Dry-run on live feeds before deploying to live trading ## Version Control Keep all versions: name files `MyStrategy_v1.py`, `MyStrategy_v2.py`, etc. Add comments above each change explaining what improved and why. This preserves your iteration history and makes reverting safe. ## References - **`references/indicators-guide.md`** — Technical indicator formulas and interpretation - **`references/iteration-workflow.md`** — Step-by-step walkthrough of strategy optimization