TrendFollowingBot

A pragmatic trend-following strategy that pairs EMA trend detection with two simple, complementary entry patterns:

1. a crossback into trend (price crosses the EMA and the very next bar opens on the trend side), and
2. an ATR-scaled breakout away from the EMA (current open is beyond the EMA by k × ATR).

Exits (initial stop, trail/TP) and sizing are delegated to the shared bot base via your chosen ExitStrategy (e.g., TrailingATRExit, FixedRatioExit), so this class focuses purely on when to enter. It reads signals from the previous closed bar and places orders at the current bar open to avoid look-ahead. :contentReference[oaicite:0]{index=0}

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Overview

• Goal: Participate in sustained moves while avoiding most chop by requiring either a confirmed cross of the trend filter (EMA) or enough volatility-adjusted distance from it.
• Trend filter: Exponential moving average of length trend_ema_span (default 50).
• Volatility gate: ATR from the prior closed bar; breakout_atr_mult (default 1.5) scales how far from the EMA price must be to count as a breakout.
• Signal timing: Compute on **bar *t-1; place the order at the **open of bar *t (no look-ahead).
• Exits & sizing: Inherited from the base strategy (risk %, SL/TP policy, trailing logic).
• Data requirements: The dataframe must include an EMA column named ema_{trend_ema_span} (e.g., ema_50) and an ATR series used by the base bot.

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Algorithm & Entry Logic

Let:

• ema_prev = EMA value on the previous (closed) bar
• prev_close = prior bar’s close
• entry_open = current bar open (where your order will execute)
• atr_prev = prior bar’s ATR (from the base helper)

1) Crossback into trend

• Long: prev_close ≤ ema_prev and entry_open > ema_prev → buy
• Short: prev_close ≥ ema_prev and entry_open < ema_prev → sell

This insists the trend filter is actually crossed and that the very next open is on the trend side—reducing whipsaw from intrabar pokes.

2) EMA-±(k × ATR) breakout
Compute a volatility buffer: threshold = breakout_atr_mult × atr_prev.

• Long: entry_open > ema_prev + threshold → buy
• Short: entry_open < ema_prev − threshold → sell

If neither rule fires, the bot stays flat for that bar.

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Key Parameters

Param	Default	What it does
trend_ema_span	50	Length of the EMA trend filter (expects a column named ema_50, etc.).
breakout_atr_mult	1.5	Volatility buffer (in ATRs) required for the EMA-distance breakout entries.

Tuning tips: Smaller breakout_atr_mult captures more breakouts (higher turnover, more noise). Larger values trade less but target stronger impulses. Shorter trend_ema_span reacts faster (more trades), longer spans are steadier.

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Notes on Exits & Risk

This bot only decides side/entry. Stops, trailing, take-profits, and position sizing are handled by the shared base (BaseStrategyBot) through the configured ExitStrategy—so you can mix and match exits (e.g., ATR-trailing) without rewriting entry logic. :contentReference[oaicite:1]{index=1}

Assests Included in Portfolio

Assest name	Notebook symbol (example)	Comment
Gold	GC=F	COMEX Gold
Silver	SI=F	COMEX Silver
U.S. Bonds	ZB=F	30-Year Treasury Bond
Crude Oil	CL=F	NYMEX WTI
Soybeans	ZS=F	CBOT Soybeans
Deutsche Mark	6E=F	Euro FX as successor to DEM
British Pound	6B=F	British Pound FX
Live Cattle	LE=F	CME Live Cattle

Setup

# jump to repo root (fallback: parent if in notebooks/)
ROOT = !git rev-parse --show-toplevel 2>/dev/null
%cd {ROOT[0] if ROOT else '..'}

/home/dennis/Algo-Trading-Stack

!./setup/fetch_sample_portfolio_futures_data.sh

========== Last 10 years ==========
⏭️  Gold: already exists, skipping.
⏭️  Silver: already exists, skipping.
⏭️  Crude_Oil: already exists, skipping.
⏭️  Soybeans: already exists, skipping.
⏭️  Sugar: already exists, skipping.
⏭️  US_Treasury_Bonds: already exists, skipping.
⏭️  Euro: already exists, skipping.
⏭️  British_Pound: already exists, skipping.
⏭️  Live_Cattle: already exists, skipping.
========== Last 20 years ==========
⏭️  Gold: already exists, skipping.
⏭️  Silver: already exists, skipping.
⏭️  Crude_Oil: already exists, skipping.
⏭️  Soybeans: already exists, skipping.
⏭️  Sugar: already exists, skipping.
⏭️  US_Treasury_Bonds: already exists, skipping.
⏭️  Euro: already exists, skipping.
⏭️  British_Pound: already exists, skipping.
⏭️  Live_Cattle: already exists, skipping.
========== 2000 to 2015 ==========
⏭️  Gold: already exists, skipping.
⏭️  Silver: already exists, skipping.
⏭️  Crude_Oil: already exists, skipping.
⏭️  Soybeans: already exists, skipping.
⏭️  Sugar: already exists, skipping.
⏭️  US_Treasury_Bonds: already exists, skipping.
⏭️  Euro: already exists, skipping.
⏭️  British_Pound: already exists, skipping.
⏭️  Live_Cattle: already exists, skipping.
✅ All downloads complete.

# Enable autoreload (useful while iterating), and hook Qt into Jupyter
%load_ext autoreload
%autoreload 2
%gui qt

Project root & imports

Set the project root if your notebook isn't at the repo root. By default, we assume the notebook lives in the root (where classes/ and bots/ exist).

import sys, os, pathlib
PROJECT_ROOT = os.path.abspath('.') 
if PROJECT_ROOT not in sys.path:
    sys.path.insert(0, PROJECT_ROOT)
print('PROJECT_ROOT =', PROJECT_ROOT)

os.chdir(PROJECT_ROOT)
print("Current working directory:", os.getcwd())

PROJECT_ROOT = /home/dennis/Algo-Trading-Stack
Current working directory: /home/dennis/Algo-Trading-Stack

from PyQt5 import QtWidgets
import gc

from classes.Backtester_Engine import BacktesterEngine
from classes.Trading_Environment import TradingEnvironment
from classes.ui_main_window import launch_gui

# Bots
from bots.coin_flip_bot.coin_flip_bot import CoinFlipBot
from bots.trend_following_bot.trend_following_bot import TrendFollowingBot

# Exits
from bots.exit_strategies import TrailingATRExit, FixedRatioExit

Build the exit strategy

exit_strategy = TrailingATRExit(atr_multiple=3.0)

Build the bot

bot = TrendFollowingBot(
    exit_strategy=exit_strategy,
    base_risk_percent=0.01,
    enforce_sessions=False,
    flatten_before_maintenance=True,
    enable_online_learning=False
)

Initialize engine and environment

config_path = "backtest_configs/backtest_config_10_yrs.yaml"

api = BacktesterEngine(config_path=config_path)
api.connect()

env = TradingEnvironment()
env.set_api(api)
env.set_bot(bot)

# Initial indicator compute happens inside TradingEnvironment on connect.
print('Assets:', env.get_asset_list())

Assets: ['6B=F', 'CL=F', '6E=F', 'GC=F', 'LE=F', 'SI=F', 'ZS=F', 'ZB=F']

Launch GUI and Run Backtest

This starts the backtest control panel and charting UI. You can open charts, start/pause/restart, and view statistics. If the window doesn't appear from within Jupyter, ensure you ran %gui qt above, or run this notebook locally (VS Code, JupyterLab).

launch_gui(env, api)

Backtesting Results

Show Statistics

# Minimal: pull stats from the running/backtested engine and show them inline

import pandas as pd
from IPython.display import display

stats = api.get_stats_snapshot()   # live snapshot; safe to call anytime

# Portfolio (one row)
display(pd.DataFrame([{
    "Initial Cash":   stats["portfolio"].get("initial_cash", 0.0),
    "Final Equity":   stats["portfolio"].get("total_equity", 0.0),
    "Used Margin":    stats["portfolio"].get("used_margin", 0.0),
    "Max Drawdown %": 100.0 * stats["portfolio"].get("max_drawdown", 0.0),
}]))

# Per-asset table
display(pd.DataFrame.from_dict(stats["per_asset"], orient="index").reset_index().rename(columns={"index":"Symbol"}))

	Initial Cash	Final Equity	Used Margin	Max Drawdown %
0	1000000.0	1.545871e+06	0.0	15.238082

	Symbol	trades	wins	losses	long_trades	short_trades	win_rate	avg_win	avg_loss	profit_factor	expectancy	commission_total	fee_total	max_drawdown
0	6B=F	83	38	45	38	45	0.457831	11652.302632	-7216.388890	1.363524	1422.289156	2100.0	249.0	0.069206
1	CL=F	72	26	46	40	32	0.361111	13742.307693	-6131.956521	1.266707	1044.861112	472.0	216.0	0.108080
2	6E=F	82	31	51	36	46	0.378049	11842.943546	-7097.058825	1.014315	63.185974	1368.0	246.0	0.118169
3	GC=F	100	39	61	55	45	0.390000	15513.333341	-6805.901640	1.457318	1898.600003	820.0	300.0	0.059000
4	LE=F	88	43	45	54	34	0.488636	11444.186049	-8417.555555	1.299137	1287.613638	1836.0	264.0	0.110060
5	SI=F	138	49	89	73	65	0.355072	11692.857146	-6729.775281	0.956591	-188.405796	1116.0	414.0	0.194345
6	ZS=F	81	28	53	40	41	0.345679	17283.035713	-7013.207546	1.301924	1385.493827	1400.0	243.0	0.127162
7	ZB=F	76	28	48	38	38	0.368421	11052.455355	-7070.963540	0.911794	-393.914473	776.0	228.0	0.107688

Show Equity Curve

# Assuming `s` is the equity Series you already built
import matplotlib.pyplot as plt
from matplotlib.ticker import FuncFormatter

# Times + equity (portfolio). Safe to call anytime; uses the engine's live history.
times, equity = api.get_equity_series()   # None -> portfolio; pass a symbol for per-asset

n = min(len(times), len(equity))
if n == 0:
    print("No equity data available yet.")
else:
    s = pd.Series(equity[:n], index=pd.to_datetime(times[:n])).dropna()

    # (Optional) smooth gaps like weekends/holidays:
    s = s.resample("h").last().ffill()

    fig, ax = plt.subplots(figsize=(10, 4))
    ax.plot(s.index, s.values)
    ax.set_title("Portfolio Equity")
    ax.set_xlabel("Time"); ax.set_ylabel("Equity ($)")
    ax.grid(True)
    
    # Turn off scientific notation/offset and format with commas
    ax.ticklabel_format(axis='y', style='plain', useOffset=False)
    ax.yaxis.set_major_formatter(FuncFormatter(lambda x, pos: f'${x:,.0f}'))
    
    fig.autofmt_xdate()
    plt.show()