├── README.md
└── bookValue.py


/README.md:
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1 | # PricePoint Public
2 | 
3 | Built in Python using Amazon, eBay, and Facebook APIs, PricePoint researches and recommends e-commerce arbitrage opportunities. PricePoint helps me find, price, and sell ~$xxk each year of books and other inventory to keep the student loan monster at bay.
4 | 
5 | Code here offered as example of book pricing from Amazon sales rank and listed price. Focus is on efficient allocation of capital in a conservative fashion.
6 | 
7 | Most of the system focuses on identification of products sold below the target prices determined by this code and other programs like it.
8 | 


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/bookValue.py:
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 1 | import pandas as pd
 2 | 
 3 | #value book given lowest list price and Amazon offer of giftcard
 4 | def bookValue(listed, redeem, salesRank):
 5 | 
 6 |     depreciation = 0.00
 7 | 
 8 |     #sales rank is a proxy for liquidity - depreciation more likely for books that take longer to turn
 9 |     if salesRank > 100000:
10 |         depreciation += 0.35
11 |     elif salesRank > 50000:
12 |         depreciation += 0.30
13 |     elif salesRank > 25000:
14 |         depreciation += 0.25
15 |     elif salesRank > 10000:
16 |         depreciation += 0.20
17 |     elif salesRank > 5000:
18 |         depreciation += 0.15
19 |     elif salesRank > 2500:
20 |         depreciation += 0.10
21 |     elif salesRank > 1000:
22 |         depreciation += 0.05
23 | 
24 |     #higher priced items more susceptible to price swings
25 |     if listed > 100:
26 |         depreciation += 0.10
27 |     elif listed > 50:
28 |         depreciation += 0.05
29 | 
30 |     #fixed costs and margin
31 |     shippingAndBox = 6
32 |     profit = 10
33 | 
34 |     #account for amazon fees
35 |     gross = 0.7 * listed
36 | 
37 |     net = gross - shippingAndBox
38 | 
39 |     #offer is max(value of amazon gift card, amount expected after margin)
40 |     if redeem > net:
41 |         offer = 0.7 * redeem
42 |     else:
43 |         offer = (net * (1 - depreciation)) - profit
44 | 
45 |     estimatedProfit = listed - offer
46 | 
47 |     if offer < 5:
48 |         return (0,0)
49 |     else:
50 |         return (offer, estimatedProfit)
51 | 
52 | #take csv of price data and generate upper bound of price to pay
53 | def bulkEstimate():
54 | 
55 |     #set data/export file
56 |     path = "YOUR PATH HERE"
57 |     export = "YOUR PATH HERE"
58 | 
59 |     df = pd.read_csv(path)
60 | 
61 |     newData = []
62 | 
63 |     #iterate over items in sheet, determine offer, record in new dataframe
64 |     index = 0
65 |     for entries in range(len(df)):
66 | 
67 |         isbn = str(df["ISBN13"][index])
68 |         isbn = isbn[0:3] + isbn[4:]
69 | 
70 |         listed = df["Retail"][index]
71 |         redeem = df["Trade In"][index]
72 |         salesRank = df["Rank"][index]
73 | 
74 |         index += 1
75 | 
76 |         (offer, estimatedProfit) = bookValue(listed, redeem, salesRank)
77 | 
78 |         newData.append((isbn, offer, estimatedProfit))
79 | 
80 |     newdf = pd.DataFrame(newData)
81 |     newdf.columns = ["ISBN13", "Offer", "Estimated Profit"]
82 | 
83 |     newdf.to_csv(export, encoding='utf-8', index = False)
84 | 
85 | bulkEstimate()
86 | 


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