roi-calculator
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ChineseROI Calculator
ROI 计算器
Comprehensive ROI calculations for marketing, investments, and business decisions.
为营销、投资和商业决策提供全面的ROI计算功能。
Features
功能特性
- Simple ROI: Basic return on investment calculation
- Marketing ROI: Campaign performance with attribution
- Investment ROI: Time-adjusted returns with CAGR
- Break-Even Analysis: Find profit threshold
- Payback Period: Time to recover investment
- Comparative Analysis: Compare multiple options
- What-If Scenarios: Sensitivity analysis
- 简易ROI计算：基础投资回报率计算
- 营销ROI计算：带有归因分析的营销活动绩效评估
- 投资ROI计算：包含复合年增长率（CAGR）的时间调整回报率
- 盈亏平衡分析：找出盈利阈值
- 投资回收期计算：收回投资所需的时间
- 对比分析：比较多个选项
- 假设场景分析：敏感性分析
Quick Start
快速开始
python
from roi_calculator import ROICalculator
calc = ROICalculator()python
from roi_calculator import ROICalculator
calc = ROICalculator()Simple ROI
Simple ROI
roi = calc.simple_roi(investment=10000, return_value=15000)
print(f"ROI: {roi['roi_percent']}%")
roi = calc.simple_roi(investment=10000, return_value=15000)
print(f"ROI: {roi['roi_percent']}%")
Marketing ROI
Marketing ROI
marketing = calc.marketing_roi(
ad_spend=5000,
revenue_generated=25000,
cost_of_goods=10000
)
print(f"Marketing ROI: {marketing['roi_percent']}%")
marketing = calc.marketing_roi(
ad_spend=5000,
revenue_generated=25000,
cost_of_goods=10000
)
print(f"Marketing ROI: {marketing['roi_percent']}%")
Break-even analysis
Break-even analysis
breakeven = calc.break_even(
fixed_costs=50000,
price_per_unit=100,
variable_cost_per_unit=60
)
print(f"Break-even units: {breakeven['units']}")
undefinedbreakeven = calc.break_even(
fixed_costs=50000,
price_per_unit=100,
variable_cost_per_unit=60
)
print(f"Break-even units: {breakeven['units']}")
undefinedCLI Usage
CLI 使用方式
bash
undefinedbash
undefinedSimple ROI
Simple ROI
python roi_calculator.py --investment 10000 --return 15000
python roi_calculator.py --investment 10000 --return 15000
Marketing campaign ROI
Marketing campaign ROI
python roi_calculator.py --marketing --spend 5000 --revenue 25000 --cogs 10000
python roi_calculator.py --marketing --spend 5000 --revenue 25000 --cogs 10000
Break-even analysis
Break-even analysis
python roi_calculator.py --breakeven --fixed 50000 --price 100 --variable 60
python roi_calculator.py --breakeven --fixed 50000 --price 100 --variable 60
Investment with time (CAGR)
Investment with time (CAGR)
python roi_calculator.py --investment 10000 --return 20000 --years 5
python roi_calculator.py --investment 10000 --return 20000 --years 5
Payback period
Payback period
python roi_calculator.py --payback --investment 100000 --annual-return 25000
python roi_calculator.py --payback --investment 100000 --annual-return 25000
Compare multiple investments
Compare multiple investments
python roi_calculator.py --compare investments.csv
undefinedpython roi_calculator.py --compare investments.csv
undefinedAPI Reference
API 参考
ROICalculator Class
ROICalculator 类
python
class ROICalculator:
def __init__(self)
# Basic ROI
def simple_roi(self, investment: float, return_value: float) -> Dict
def net_roi(self, investment: float, gain: float, costs: float = 0) -> Dict
# Marketing ROI
def marketing_roi(self, ad_spend: float, revenue_generated: float,
cost_of_goods: float = 0) -> Dict
def campaign_roi(self, campaigns: List[Dict]) -> pd.DataFrame
def roas(self, ad_spend: float, revenue: float) -> Dict # Return on Ad Spend
# Investment ROI
def investment_roi(self, initial: float, final: float, years: float) -> Dict
def cagr(self, initial: float, final: float, years: float) -> float
def total_return(self, initial: float, final: float, dividends: float = 0) -> Dict
# Break-Even Analysis
def break_even(self, fixed_costs: float, price_per_unit: float,
variable_cost_per_unit: float) -> Dict
def break_even_revenue(self, fixed_costs: float, contribution_margin_ratio: float) -> Dict
# Payback Period
def payback_period(self, investment: float, annual_cash_flow: float) -> Dict
def payback_period_uneven(self, investment: float, cash_flows: List[float]) -> Dict
# Comparative Analysis
def compare_investments(self, investments: List[Dict]) -> pd.DataFrame
def rank_by_roi(self, investments: List[Dict]) -> List[Dict]
# Sensitivity Analysis
def sensitivity_analysis(self, base_case: Dict, variables: Dict) -> pd.DataFrame
def scenario_analysis(self, scenarios: List[Dict]) -> pd.DataFrame
# Reporting
def generate_report(self, analysis: Dict, output: str) -> strpython
class ROICalculator:
def __init__(self)
# Basic ROI
def simple_roi(self, investment: float, return_value: float) -> Dict
def net_roi(self, investment: float, gain: float, costs: float = 0) -> Dict
# Marketing ROI
def marketing_roi(self, ad_spend: float, revenue_generated: float,
cost_of_goods: float = 0) -> Dict
def campaign_roi(self, campaigns: List[Dict]) -> pd.DataFrame
def roas(self, ad_spend: float, revenue: float) -> Dict # Return on Ad Spend
# Investment ROI
def investment_roi(self, initial: float, final: float, years: float) -> Dict
def cagr(self, initial: float, final: float, years: float) -> float
def total_return(self, initial: float, final: float, dividends: float = 0) -> Dict
# Break-Even Analysis
def break_even(self, fixed_costs: float, price_per_unit: float,
variable_cost_per_unit: float) -> Dict
def break_even_revenue(self, fixed_costs: float, contribution_margin_ratio: float) -> Dict
# Payback Period
def payback_period(self, investment: float, annual_cash_flow: float) -> Dict
def payback_period_uneven(self, investment: float, cash_flows: List[float]) -> Dict
# Comparative Analysis
def compare_investments(self, investments: List[Dict]) -> pd.DataFrame
def rank_by_roi(self, investments: List[Dict]) -> List[Dict]
# Sensitivity Analysis
def sensitivity_analysis(self, base_case: Dict, variables: Dict) -> pd.DataFrame
def scenario_analysis(self, scenarios: List[Dict]) -> pd.DataFrame
# Reporting
def generate_report(self, analysis: Dict, output: str) -> strROI Calculations
ROI 计算方法
Simple ROI
简易ROI计算
python
result = calc.simple_roi(investment=10000, return_value=15000)python
result = calc.simple_roi(investment=10000, return_value=15000)Returns:
Returns:
{
{
"investment": 10000,
"investment": 10000,
"return_value": 15000,
"return_value": 15000,
"gain": 5000,
"gain": 5000,
"roi_percent": 50.0,
"roi_percent": 50.0,
"roi_ratio": 0.5
"roi_ratio": 0.5
}
}
undefinedundefinedNet ROI (with additional costs)
净ROI计算（含额外成本）
python
result = calc.net_roi(
investment=10000,
gain=8000,
costs=2000 # Additional costs
)python
result = calc.net_roi(
investment=10000,
gain=8000,
costs=2000 # Additional costs
)Returns:
Returns:
{
{
"investment": 10000,
"investment": 10000,
"gross_gain": 8000,
"gross_gain": 8000,
"costs": 2000,
"costs": 2000,
"net_gain": 6000,
"net_gain": 6000,
"roi_percent": 60.0
"roi_percent": 60.0
}
}
undefinedundefinedMarketing ROI
营销ROI计算
Campaign ROI
营销活动ROI
python
result = calc.marketing_roi(
ad_spend=5000,
revenue_generated=25000,
cost_of_goods=10000
)python
result = calc.marketing_roi(
ad_spend=5000,
revenue_generated=25000,
cost_of_goods=10000
)Returns:
Returns:
{
{
"ad_spend": 5000,
"ad_spend": 5000,
"revenue": 25000,
"revenue": 25000,
"cost_of_goods": 10000,
"cost_of_goods": 10000,
"gross_profit": 15000,
"gross_profit": 15000,
"net_profit": 10000, # After ad spend
"net_profit": 10000, # After ad spend
"roi_percent": 200.0,
"roi_percent": 200.0,
"roas": 5.0 # $5 revenue per $1 ad spend
"roas": 5.0 # $5 revenue per $1 ad spend
}
}
undefinedundefinedReturn on Ad Spend (ROAS)
广告支出回报率（ROAS）
python
result = calc.roas(ad_spend=1000, revenue=4000)python
result = calc.roas(ad_spend=1000, revenue=4000)Returns:
Returns:
{
{
"ad_spend": 1000,
"ad_spend": 1000,
"revenue": 4000,
"revenue": 4000,
"roas": 4.0,
"roas": 4.0,
"roas_percent": 400.0
"roas_percent": 400.0
}
}
undefinedundefinedMulti-Campaign Comparison
多营销活动对比
python
campaigns = [
{"name": "Facebook", "spend": 2000, "revenue": 8000, "cogs": 3000},
{"name": "Google", "spend": 3000, "revenue": 15000, "cogs": 6000},
{"name": "Email", "spend": 500, "revenue": 3000, "cogs": 1000}
]
results = calc.campaign_roi(campaigns)python
campaigns = [
{"name": "Facebook", "spend": 2000, "revenue": 8000, "cogs": 3000},
{"name": "Google", "spend": 3000, "revenue": 15000, "cogs": 6000},
{"name": "Email", "spend": 500, "revenue": 3000, "cogs": 1000}
]
results = calc.campaign_roi(campaigns)Returns DataFrame with ROI for each campaign
Returns DataFrame with ROI for each campaign
undefinedundefinedInvestment ROI
投资ROI计算
Time-Adjusted ROI with CAGR
含CAGR的时间调整ROI
python
result = calc.investment_roi(
initial=10000,
final=20000,
years=5
)python
result = calc.investment_roi(
initial=10000,
final=20000,
years=5
)Returns:
Returns:
{
{
"initial": 10000,
"initial": 10000,
"final": 20000,
"final": 20000,
"total_gain": 10000,
"total_gain": 10000,
"total_roi_percent": 100.0,
"total_roi_percent": 100.0,
"cagr_percent": 14.87, # Compound Annual Growth Rate
"cagr_percent": 14.87, # Compound Annual Growth Rate
"years": 5
"years": 5
}
}
undefinedundefinedTotal Return with Dividends
含股息的总回报率
python
result = calc.total_return(
initial=10000,
final=12000,
dividends=1500
)python
result = calc.total_return(
initial=10000,
final=12000,
dividends=1500
)Returns:
Returns:
{
{
"initial": 10000,
"initial": 10000,
"final": 12000,
"final": 12000,
"capital_gain": 2000,
"capital_gain": 2000,
"dividends": 1500,
"dividends": 1500,
"total_return": 3500,
"total_return": 3500,
"total_return_percent": 35.0
"total_return_percent": 35.0
}
}
undefinedundefinedBreak-Even Analysis
盈亏平衡分析
Unit Break-Even
单位盈亏平衡
python
result = calc.break_even(
fixed_costs=50000,
price_per_unit=100,
variable_cost_per_unit=60
)python
result = calc.break_even(
fixed_costs=50000,
price_per_unit=100,
variable_cost_per_unit=60
)Returns:
Returns:
{
{
"fixed_costs": 50000,
"fixed_costs": 50000,
"price_per_unit": 100,
"price_per_unit": 100,
"variable_cost_per_unit": 60,
"variable_cost_per_unit": 60,
"contribution_margin": 40,
"contribution_margin": 40,
"contribution_margin_ratio": 0.4,
"contribution_margin_ratio": 0.4,
"break_even_units": 1250,
"break_even_units": 1250,
"break_even_revenue": 125000
"break_even_revenue": 125000
}
}
undefinedundefinedRevenue Break-Even
营收盈亏平衡
python
result = calc.break_even_revenue(
fixed_costs=50000,
contribution_margin_ratio=0.4 # 40% margin
)python
result = calc.break_even_revenue(
fixed_costs=50000,
contribution_margin_ratio=0.4 # 40% margin
)Returns:
Returns:
{
{
"fixed_costs": 50000,
"fixed_costs": 50000,
"contribution_margin_ratio": 0.4,
"contribution_margin_ratio": 0.4,
"break_even_revenue": 125000
"break_even_revenue": 125000
}
}
undefinedundefinedPayback Period
投资回收期计算
Simple Payback
简易投资回收期
python
result = calc.payback_period(
investment=100000,
annual_cash_flow=25000
)python
result = calc.payback_period(
investment=100000,
annual_cash_flow=25000
)Returns:
Returns:
{
{
"investment": 100000,
"investment": 100000,
"annual_cash_flow": 25000,
"annual_cash_flow": 25000,
"payback_years": 4.0,
"payback_years": 4.0,
"payback_months": 48
"payback_months": 48
}
}
undefinedundefinedUneven Cash Flows
非均匀现金流投资回收期
python
result = calc.payback_period_uneven(
investment=100000,
cash_flows=[20000, 30000, 40000, 35000, 25000] # Per year
)python
result = calc.payback_period_uneven(
investment=100000,
cash_flows=[20000, 30000, 40000, 35000, 25000] # Per year
)Returns:
Returns:
{
{
"investment": 100000,
"investment": 100000,
"payback_years": 2.75, # Recovered in year 3
"payback_years": 2.75, # Recovered in year 3
"cumulative_flows": [20000, 50000, 90000, 125000, 150000]
"cumulative_flows": [20000, 50000, 90000, 125000, 150000]
}
}
undefinedundefinedComparative Analysis
对比分析
Compare Multiple Investments
多投资选项对比
python
investments = [
{"name": "Project A", "investment": 50000, "return": 75000, "years": 2},
{"name": "Project B", "investment": 30000, "return": 48000, "years": 3},
{"name": "Project C", "investment": 100000, "return": 180000, "years": 5}
]
comparison = calc.compare_investments(investments)python
investments = [
{"name": "Project A", "investment": 50000, "return": 75000, "years": 2},
{"name": "Project B", "investment": 30000, "return": 48000, "years": 3},
{"name": "Project C", "investment": 100000, "return": 180000, "years": 5}
]
comparison = calc.compare_investments(investments)Returns DataFrame:
Returns DataFrame:
name | investment | return | roi% | cagr% | payback
name | investment | return | roi% | cagr% | payback
Project A | 50000 | 75000 | 50.0 | 22.5 | 1.33
Project A | 50000 | 75000 | 50.0 | 22.5 | 1.33
Project B | 30000 | 48000 | 60.0 | 17.0 | 1.88
Project B | 30000 | 48000 | 60.0 | 17.0 | 1.88
Project C | 100000 | 180000 | 80.0 | 12.5 | 2.78
Project C | 100000 | 180000 | 80.0 | 12.5 | 2.78
undefinedundefinedRank by ROI
按ROI排序
python
ranked = calc.rank_by_roi(investments)python
ranked = calc.rank_by_roi(investments)Returns investments sorted by ROI percentage
Returns investments sorted by ROI percentage
undefinedundefinedSensitivity Analysis
敏感性分析
Variable Sensitivity
变量敏感性分析
python
base_case = {
"fixed_costs": 50000,
"price_per_unit": 100,
"variable_cost_per_unit": 60,
"units_sold": 2000
}python
base_case = {
"fixed_costs": 50000,
"price_per_unit": 100,
"variable_cost_per_unit": 60,
"units_sold": 2000
}Test impact of price changes
Test impact of price changes
sensitivity = calc.sensitivity_analysis(
base_case=base_case,
variables={
"price_per_unit": [80, 90, 100, 110, 120],
"units_sold": [1500, 1750, 2000, 2250, 2500]
}
)
sensitivity = calc.sensitivity_analysis(
base_case=base_case,
variables={
"price_per_unit": [80, 90, 100, 110, 120],
"units_sold": [1500, 1750, 2000, 2250, 2500]
}
)
Returns DataFrame showing profit at each combination
Returns DataFrame showing profit at each combination
undefinedundefinedScenario Analysis
场景分析
python
scenarios = [
{"name": "Pessimistic", "units": 1500, "price": 90},
{"name": "Base", "units": 2000, "price": 100},
{"name": "Optimistic", "units": 2500, "price": 110}
]
results = calc.scenario_analysis(scenarios)python
scenarios = [
{"name": "Pessimistic", "units": 1500, "price": 90},
{"name": "Base", "units": 2000, "price": 100},
{"name": "Optimistic", "units": 2500, "price": 110}
]
results = calc.scenario_analysis(scenarios)Example Workflows
示例工作流
Marketing Campaign Analysis
营销活动分析
python
calc = ROICalculator()python
calc = ROICalculator()Analyze Q4 campaigns
Analyze Q4 campaigns
campaigns = [
{"name": "Black Friday Email", "spend": 2000, "revenue": 45000, "cogs": 20000},
{"name": "Holiday Facebook", "spend": 8000, "revenue": 35000, "cogs": 14000},
{"name": "Google Shopping", "spend": 5000, "revenue": 28000, "cogs": 12000}
]
results = calc.campaign_roi(campaigns)
print(results.sort_values("roi_percent", ascending=False))
campaigns = [
{"name": "Black Friday Email", "spend": 2000, "revenue": 45000, "cogs": 20000},
{"name": "Holiday Facebook", "spend": 8000, "revenue": 35000, "cogs": 14000},
{"name": "Google Shopping", "spend": 5000, "revenue": 28000, "cogs": 12000}
]
results = calc.campaign_roi(campaigns)
print(results.sort_values("roi_percent", ascending=False))
Find best performing campaign
Find best performing campaign
best = results.loc[results["roi_percent"].idxmax()]
print(f"Best ROI: {best['name']} at {best['roi_percent']:.1f}%")
undefinedbest = results.loc[results["roi_percent"].idxmax()]
print(f"Best ROI: {best['name']} at {best['roi_percent']:.1f}%")
undefinedBusiness Investment Decision
商业投资决策
python
calc = ROICalculator()python
calc = ROICalculator()Compare expansion options
Compare expansion options
options = [
{"name": "New Location", "investment": 200000, "annual_return": 45000},
{"name": "Equipment Upgrade", "investment": 75000, "annual_return": 20000},
{"name": "Digital Marketing", "investment": 30000, "annual_return": 12000}
]
for opt in options:
payback = calc.payback_period(opt["investment"], opt["annual_return"])
roi = calc.simple_roi(opt["investment"], opt["investment"] + opt["annual_return"] * 5)
print(f"{opt['name']}: Payback={payback['payback_years']:.1f}yr, 5yr ROI={roi['roi_percent']:.1f}%")
undefinedoptions = [
{"name": "New Location", "investment": 200000, "annual_return": 45000},
{"name": "Equipment Upgrade", "investment": 75000, "annual_return": 20000},
{"name": "Digital Marketing", "investment": 30000, "annual_return": 12000}
]
for opt in options:
payback = calc.payback_period(opt["investment"], opt["annual_return"])
roi = calc.simple_roi(opt["investment"], opt["investment"] + opt["annual_return"] * 5)
print(f"{opt['name']}: Payback={payback['payback_years']:.1f}yr, 5yr ROI={roi['roi_percent']:.1f}%")
undefinedPricing Strategy Analysis
定价策略分析
python
calc = ROICalculator()python
calc = ROICalculator()Break-even at different price points
Break-even at different price points
fixed_costs = 100000
variable_cost = 25
for price in [40, 50, 60, 75, 100]:
be = calc.break_even(fixed_costs, price, variable_cost)
print(f"Price ${price}: Break-even at {be['break_even_units']:,} units (${be['break_even_revenue']:,.0f})")
undefinedfixed_costs = 100000
variable_cost = 25
for price in [40, 50, 60, 75, 100]:
be = calc.break_even(fixed_costs, price, variable_cost)
print(f"Price ${price}: Break-even at {be['break_even_units']:,} units (${be['break_even_revenue']:,.0f})")
undefinedDependencies
依赖项
- pandas>=2.0.0
- numpy>=1.24.0
- pandas>=2.0.0
- numpy>=1.24.0