backtrader

Name: backtrader
Author: agiprolabs

235stars53forks

How to install backtrader?

agiprolabs/claude-trading-skills/backtrader

$npx -y skills add agiprolabs/claude-trading-skills --skill backtrader

Installs into the current project.

Summary

Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators

Files · 1

View on GitHub

SKILL.md

1	# Backtrader
2
3	Backtrader is a Python event-driven backtesting framework that processes data bar-by-bar, simulating realistic execution with a built-in broker, order management, and position tracking. Unlike vectorized frameworks (vectorbt, pandas), backtrader walks through history one bar at a time, firing callbacks that let you implement complex order logic that depends on previous fills, partial executions, and conditional brackets.
4
5	## Event-Driven vs Vectorized
6
7	\| Aspect \| Backtrader (event-driven) \| vectorbt (vectorized) \|
8	\|---\|---\|---\|
9	\| Execution model \| Bar-by-bar callbacks \| Whole-array operations \|
10	\| Speed \| Slower (Python loop) \| Fast (NumPy/Numba) \|
11	\| Order types \| Market, limit, stop, stop-limit, bracket, OCO \| Market only (native) \|
12	\| Realism \| Built-in broker with commission, slippage, margin \| Manual slippage modeling \|
13	\| Multi-timeframe \| Native resampledata \| Manual alignment \|
14	\| Best for \| Complex strategies, bracket orders, portfolio \| Fast parameter sweeps, simple signals \|
15
16	Use backtrader when you need:
17	- Bracket orders (entry + stop loss + take profit as a unit)
18	- Stop-limit or trailing stop orders
19	- Order-dependent logic (scale in after first fill, cancel if not filled in N bars)
20	- Multi-timeframe strategies (daily signals, hourly execution)
21	- Realistic commission and slippage modeling
22
23	Use vectorbt when you need:
24	- Fast parameter optimization over thousands of combinations
25	- Simple long/short signals without complex order management
26	- Quick prototyping and statistical analysis of results
27
28	---
29
30	## Core Concepts
31
32	Backtrader has five core objects that interact through an event loop:
33
34	### 1. Cerebro (the engine)
35
36	The central orchestrator. You add strategies, data feeds, analyzers, and sizers to Cerebro, then call `run()`.
37
38	```python
39	import backtrader as bt
40
41	cerebro = bt.Cerebro()
42	cerebro.addstrategy(MyStrategy, fast_period=10, slow_period=30)
43	cerebro.adddata(data_feed)
44	cerebro.broker.setcash(100_000)
45	cerebro.broker.setcommission(commission=0.003) # 0.3%
46	cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe")
47	cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
48	cerebro.run()
49	```
50
51	### 2. Strategy (your logic)
52
53	A Strategy subclass contains all trading logic. Key methods:
54
55	- `__init__()` — Define indicators. Runs once before backtesting starts.
56	- `next()` — Called on every bar. Place orders here.
57	- `notify_order(order)` — Called when order status changes (submitted, accepted, completed, canceled, margin, expired).
58	- `notify_trade(trade)` — Called when a trade opens or closes. Access P&L here.
59
60	```python
61	class EMACrossover(bt.Strategy):
62	params = (
63	("fast_period", 10),
64	("slow_period", 30),
65	)
66
67	def __init__(self) -> None:
68	self.ema_fast = bt.ind.EMA(period=self.p.fast_period)
69	self.ema_slow = bt.ind.EMA(period=self.p.slow_period)
70	self.crossover = bt.ind.CrossOver(self.ema_fast, self.ema_slow)
71
72	def next(self) -> None:
73	if not self.position:
74	if self.crossover > 0:
75	self.buy()
76	elif self.crossover < 0:
77	self.close()
78	```
79
80	### 3. Data Feed
81
82	Backtrader data feeds provide OHLCV lines. The most common approach is loading from a pandas DataFrame:
83
84	```python
85	import pandas as pd
86
87	df = pd.DataFrame({
88	"open": [...], "high": [...], "low": [...],
89	"close": [...], "volume": [...],
90	}, index=pd.DatetimeIndex([...]))
91
92	data = bt.feeds.PandasData(dataname=df)
93	cerebro.adddata(data)
94	```
95
96	For CSV files:
97
98	```python
99	data = bt.feeds.GenericCSVData(
100	dataname="ohlcv.csv",
101	dtformat="%Y-%m-%d",
102	openinterest=-1, # no open interest column
103	)
104	```
105
106	### 4. Broker
107
108	The built-in broker simulates order execution with configurable cash, commission, and slippage.
109
110	```python
111	cerebro.broker.setcash(100_000)
112	cerebro.broker.setcommission(commission=0.003) # 0.3% per trade
113
114	# Cheat-on-open: execute at the open of the signal bar (avoids lookahead)
115	cerebro.broker.set_coo(True)
116	```
117
118	### 5. Analyzers
119
120	Analyzers compute performance metrics after the backtest completes.
121
122	```python
123	cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe",
124	riskfreerate=0.0, annualize=True, timeframe=bt.TimeFrame.Days)
125	cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
126	cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
127	cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")
128
129	results = cerebro.run()
130	strat = results[0]
131
132	sharpe = strat.analyzers.sharpe.get_analysis()
133	dd = strat.analyzers.drawdown.get_analysis()
134	trades = strat.analyzers.trades.get_analysis()
135	```
136
137	---
138
139	## Order Types
140
141	Backtrader supports complex order types critical for realistic crypto backtesting.
142
143	### Market Order
144
145	```python
146	self.buy() # market buy
147	self.sell() # market sell
148	self.close() # close current position
149	```
150
151	### Limit Order
152
153	```python
154	self.buy

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backtrader

235stars53forks

View on GitHub

How to install backtrader?

agiprolabs/claude-trading-skills/backtrader

$npx -y skills add agiprolabs/claude-trading-skills --skill backtrader

Installs into the current project.

Summary

Event-driven backtesting with bar-by-bar execution, complex order types, multiple analyzers, and custom indicators

Files · 1

View on GitHub

SKILL.md

1	# Backtrader
2
3	Backtrader is a Python event-driven backtesting framework that processes data bar-by-bar, simulating realistic execution with a built-in broker, order management, and position tracking. Unlike vectorized frameworks (vectorbt, pandas), backtrader walks through history one bar at a time, firing callbacks that let you implement complex order logic that depends on previous fills, partial executions, and conditional brackets.
4
5	## Event-Driven vs Vectorized
6
7	\| Aspect \| Backtrader (event-driven) \| vectorbt (vectorized) \|
8	\|---\|---\|---\|
9	\| Execution model \| Bar-by-bar callbacks \| Whole-array operations \|
10	\| Speed \| Slower (Python loop) \| Fast (NumPy/Numba) \|
11	\| Order types \| Market, limit, stop, stop-limit, bracket, OCO \| Market only (native) \|
12	\| Realism \| Built-in broker with commission, slippage, margin \| Manual slippage modeling \|
13	\| Multi-timeframe \| Native resampledata \| Manual alignment \|
14	\| Best for \| Complex strategies, bracket orders, portfolio \| Fast parameter sweeps, simple signals \|
15
16	Use backtrader when you need:
17	- Bracket orders (entry + stop loss + take profit as a unit)
18	- Stop-limit or trailing stop orders
19	- Order-dependent logic (scale in after first fill, cancel if not filled in N bars)
20	- Multi-timeframe strategies (daily signals, hourly execution)
21	- Realistic commission and slippage modeling
22
23	Use vectorbt when you need:
24	- Fast parameter optimization over thousands of combinations
25	- Simple long/short signals without complex order management
26	- Quick prototyping and statistical analysis of results
27
28	---
29
30	## Core Concepts
31
32	Backtrader has five core objects that interact through an event loop:
33
34	### 1. Cerebro (the engine)
35
36	The central orchestrator. You add strategies, data feeds, analyzers, and sizers to Cerebro, then call `run()`.
37
38	```python
39	import backtrader as bt
40
41	cerebro = bt.Cerebro()
42	cerebro.addstrategy(MyStrategy, fast_period=10, slow_period=30)
43	cerebro.adddata(data_feed)
44	cerebro.broker.setcash(100_000)
45	cerebro.broker.setcommission(commission=0.003) # 0.3%
46	cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe")
47	cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
48	cerebro.run()
49	```
50
51	### 2. Strategy (your logic)
52
53	A Strategy subclass contains all trading logic. Key methods:
54
55	- `__init__()` — Define indicators. Runs once before backtesting starts.
56	- `next()` — Called on every bar. Place orders here.
57	- `notify_order(order)` — Called when order status changes (submitted, accepted, completed, canceled, margin, expired).
58	- `notify_trade(trade)` — Called when a trade opens or closes. Access P&L here.
59
60	```python
61	class EMACrossover(bt.Strategy):
62	params = (
63	("fast_period", 10),
64	("slow_period", 30),
65	)
66
67	def __init__(self) -> None:
68	self.ema_fast = bt.ind.EMA(period=self.p.fast_period)
69	self.ema_slow = bt.ind.EMA(period=self.p.slow_period)
70	self.crossover = bt.ind.CrossOver(self.ema_fast, self.ema_slow)
71
72	def next(self) -> None:
73	if not self.position:
74	if self.crossover > 0:
75	self.buy()
76	elif self.crossover < 0:
77	self.close()
78	```
79
80	### 3. Data Feed
81
82	Backtrader data feeds provide OHLCV lines. The most common approach is loading from a pandas DataFrame:
83
84	```python
85	import pandas as pd
86
87	df = pd.DataFrame({
88	"open": [...], "high": [...], "low": [...],
89	"close": [...], "volume": [...],
90	}, index=pd.DatetimeIndex([...]))
91
92	data = bt.feeds.PandasData(dataname=df)
93	cerebro.adddata(data)
94	```
95
96	For CSV files:
97
98	```python
99	data = bt.feeds.GenericCSVData(
100	dataname="ohlcv.csv",
101	dtformat="%Y-%m-%d",
102	openinterest=-1, # no open interest column
103	)
104	```
105
106	### 4. Broker
107
108	The built-in broker simulates order execution with configurable cash, commission, and slippage.
109
110	```python
111	cerebro.broker.setcash(100_000)
112	cerebro.broker.setcommission(commission=0.003) # 0.3% per trade
113
114	# Cheat-on-open: execute at the open of the signal bar (avoids lookahead)
115	cerebro.broker.set_coo(True)
116	```
117
118	### 5. Analyzers
119
120	Analyzers compute performance metrics after the backtest completes.
121
122	```python
123	cerebro.addanalyzer(bt.analyzers.SharpeRatio, _name="sharpe",
124	riskfreerate=0.0, annualize=True, timeframe=bt.TimeFrame.Days)
125	cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
126	cerebro.addanalyzer(bt.analyzers.TradeAnalyzer, _name="trades")
127	cerebro.addanalyzer(bt.analyzers.Returns, _name="returns")
128
129	results = cerebro.run()
130	strat = results[0]
131
132	sharpe = strat.analyzers.sharpe.get_analysis()
133	dd = strat.analyzers.drawdown.get_analysis()
134	trades = strat.analyzers.trades.get_analysis()
135	```
136
137	---
138
139	## Order Types
140
141	Backtrader supports complex order types critical for realistic crypto backtesting.
142
143	### Market Order
144
145	```python
146	self.buy() # market buy
147	self.sell() # market sell
148	self.close() # close current position
149	```
150
151	### Limit Order
152
153	```python
154	self.buy

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